BACk Pain in Elders in Norway (BACE-N); A Prospective Cohort Study of Older People Visiting Primary Care With a New Episode of Back Pain
Conditions: Back Pain
Sponsor: Oslo Metropolitan University

Statistical analysis plan (SAP) for the project:  
BACk pain in Elders in Norway (BACE-N): a prospective cohort study 
of older people visiting primary care with a new episode of back pain 
 
 
 
 
 
 
 
 
 
 
 
SAP Authors:  
Rikke Munk Killingmo, Ørjan Nesse Vigdal,  Lise Kretz, Kjersti Storheim, Are Hugo Pripp,  
Margreth Grotle (order not decided) 
 
 
NCT identifier: NCT04261309 
Document date: 21.02.2024 
 

 
 
 
 
 
Statistical analysis plan (SAP) for:  
Healthcare utilization and related costs among older people seeking 
primary care due to back pain: a prospective cohort study with one 
year of follow up (working title)  
 
 
 
Project:  
 
BACk pain in Elders in Norway (BACE-N) (BACE-N) 
NCT number:   
NCT04261309   
Document date: 
11.03.2021 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
1. Introduction to SAP  
1.1 Scope 
This document is a supplement to the BACE-N protocol (ClinicalTrials.gov Identifier: 
NCT04261309) and comprises a SAP for the article “Healthcare utilization and related cost 
among older people seeking primary care due to back pain: a prospective cohort study with 
one year of follow up”. The current SAP has been written while data collection was ongoing 
(we had access to baseline data, but not to follow-up data) and it will be uploaded to the 
ClinicalTrials.gov before full access to the study database. 
2. Administrative information 
Version of SAP 
1.0 
Study sponsor 
Oslo Metropolitan University, the Norwegian Fund for Post-Graduate Training in 
Physiotherapy and “Et liv i bevegelse” (A life in movement) - Norwegian chiropractors’ 
research foundation 
 
Names, affiliations and roles of SAP contributors  
 
Name 
Title 
Affiliation 
Role  
Margreth Grotle 
Professor 
Oslo Metropolitan University 
P44, 0167 Oslo 
Phone: 90111172  
Email: mgrotle@oslomet.no 
Principal Investigator  
Rikke Munk Killingmo 
PhD-student Oslo Metropolitan University Main author of SAP  
Kjersti Storheim  
Professor 
Oslo Metropolitan University Contributor to SAP 
Daniëlle van der Windt Professor  
Keele University 
Contributor to SAP 
Milada C. Småstuen  
Professor 
Oslo Metropolitan University Statistical advisor  
Ørjan Nesse Vigdal 
PhD-student Oslo Metropolitan University Contributor to SAP 
Lise Kretz 
PhD-student Oslo Metropolitan University Contributor to SAP 
 
Signatures: person writing SAP, senior statistician and principal investigator  
 
 
__________________________ __________________________ ________________________ 
Rikke Munk Killingmo 
Milada C. Småstuen 
Margreth Grotle 
Main author of the SAP 
Senior statistician responsible 
Principal Investigator 

SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
3. Study aim 
The primary aim of this study is to describe healthcare utilization and estimate associated 
costs during one year of follow up of older people seeking primary care due to a new 
episode of back pain. The secondary aim is to describe frequency of healthcare utilization 
across patients with different risk profile stratified according to the StarT Back Screening 
tool.  
4. Study design, population and method  
Study design  
A prospective observational cohort study with one year of follow-up within a Norwegian 
primary care setting.  
Study population and recruitment 
Eligible patients are people 55 years of age or older who seek primary care (physiotherapist, 
chiropractor or GP) with a new episode of back pain (preceded by 6 months without visiting 
a primary care provider for similar complaints). Patients are excluded if they have difficulties 
completing the questionnaires (e.g. unable to speak, read or write in Norwegian) or if they 
have difficulties completing the physical examination (e.g. are wheelchair bound). 
Patients were recruited from physiotherapists, chiropractors and GPs working in Norwegian 
primary care between April 2015 and February 2020. Patients who met the eligibility criteria 
and completed the consent to participate were included in the study.  
Method  
At baseline all patients completed a comprehensive questionnaire and underwent a 
standardized physical examination conducted by local research assistants at test stations 
established within each recruiting area. Follow-up questionnaires will be sent at 3, 6, and 12 
months after inclusion for completion at home. All questionnaires are preferably completed 
electronically using the Infopad system, but paper versions will be available for patients not 
familiar with electronic data collection. All information will be stored and analysed securely 
through Service for sensitive data (TSD) at the University of Oslo, Norway.      
Variables 
Outcome variables 
The primary outcome of this study is total costs of healthcare utilization (summed up for one 
year of follow-up). The secondary outcome is frequency of healthcare utilization (summed 
up for one-year of follow-up across patients with different risk profile according to the StarT 
Back Screening tool).   
Healthcare utilization will be self-reported, collected through follow-up questionnaires, and 
include; consultation to healthcare professionals (type and frequency), number of diagnostic 
examinations (type and frequency), number of days of hospitalization and/or 
institutionalisation, back operations and use of back medication (both prescription and over-
the-counter, type and frequency). All variables, except the “operation” variable, will be 

SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
reported with a 3 month recall period at 3, 6, and 12 months follow-up. The “operation” 
variable will be reported with a 12 month recall period at 12 months follow-up. Healthcare 
utilization during the one year of follow-up will be described as shown in table 3 and table 5. 
The total cost of healthcare utilization will be estimated based on information presented in 
table 3 and unit costs of healthcare resources collected from national pricelists (see table 1). 
Screening tool 
The Keele StarT Back Screening Tool (SBST) [1] will be used to classify the included patients 
into low, medium or high risk of poor disability outcome. The SBST is a brief 9-item tool 
designed to screen primary care patients with low back pain for prognostic indicators that 
are relevant to initial decision making. The tool is summed to produce an overall score from 
zero to nine and a psychological subscale score from zero to five. Patients with an overall 
score between 0-3 are classified as low risk. Patients with an overall score of minimum 4 and 
a subscale score of maximum 3 are classified as medium risk. Patient with an overall score of 
minimum 4 and a subscale score of 4 or 5 are classified as high risk. The SBST was translated 
into Norwegian by Storheim and Grotle in 2012, and has shown to have an acceptable 
accuracy in distinguishing between low back pain patients who have recovered or not after 1 
year of follow-up [2]. 
Other variables 
Included patients will be described with respect to the following baseline characteristics: 
age, gender, ethnicity, educational level, first healthcare provider, pain location, pain 
severity, pain duration, pain history, disability, comorbidity, health-related quality of life, 
emotional well-being, kinesiophobia and healthcare utilization six weeks prior to inclusion 
(table 2).   
The Numeric Rating Scale (NRS) will be used to measure average pain severity last week [3]. 
The NRS, scored from 0 (no pain) to 10 (maximum pain), has been widely used to evaluate 
pain and has proven to be preferable when examining low back pain patients [4], also for 
Norwegian patients [5].  
The Roland Morris Disability Questionnaire (RMDQ) [6] will be used to measure disability. 
The RMDQ is a widely used back-specific patient-reported measure of pain-related disability 
(0 = no disability, 24 = totally disabled). The Norwegian version has been validated and found 
to have good measurement properties when used among patients with low back pain [5, 7].  
The Self-Administered Comorbidity Questionnaire (SCQ) [8] will be used to assess 
comorbidity. The SCQ is a 14-item measure of comorbidity for clinical and health services 
research settings. An individual can receive a maximum of 3 points for each medical 
condition: 1 point for the presence of the problem, another point if he/she receives 
treatment for it, and an additional point if the problem causes a limitation in functioning. 
Because there are 12 defined medical problems and 3 optional conditions, the maximum 
score totals 45 points if the open‐ended items are used and 36 points if only the close‐ended 
items are used.  
The Short Form-36 Health Status Questionnaire (SF36) [9] will be used to assess health-
related quality of life. The SF36 consist of 36 items. It measures health on eight multi-item 

SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
dimensions, covering physical functioning, social functioning, role limitations (physical 
problems), role limitations (emotional problems), mental health, vitality, pain, and overall 
evaluation of health [9]. Data-completeness of the SF36 in the general population in Norway 
seems to strongly declined with increasing age [10]. Hence, caution should be exercised 
when assessing subjective health or employing the norms among subjects aged 70 years or 
over [10].  
The Center for Epidemiologic Studies Depression Scale (CES-D) will be used to assess 
emotional well-being. The CES-D has been widely used in studies of late-life depression. 
Psychometric properties are generally favourable [11]. The Norwegian version of the CES-D 
has been used among older patients in order to measure depression symptoms [12].  
The Fear Avoidance Beliefs Questionnaire, physical activity subscale (FABQ-PA) [13] will be 
used to assess kinesiophobia. The FABQ-PA consists of four questions aimed towards 
physical activity, scored on a 7-point ordinal scale, which are summed up to a sum score, 
ranging from 0 (no fear) to 24 (maximum fear). The questionnaire has been translated into 
Norwegian and has shown acceptable psychometric properties in Norwegian patients with 
low back pain [14].  
5. Statistical analyses 
General analysis considerations  
All analyses described in this plan are considered a priori in that they have been defined in 
the protocol and/or in this SAP. All post hoc analyses will be identified as such in the article if 
relevant. All analyses will be carried out by a PhD-student using SPSS version 26 and 
controlled by a senior researcher/statistician. All statistical tests will be two-sided, and 
nominal p-values will be reported. Preliminary analyses assessing the influence of missing 
data and assumptions of normality for continuous variables will be conducted. The 
assumption of normal distribution will be investigated using histograms and QQ-plots. 
Normally distributed data will be presented with means and standard deviations (SDs), 
skewed data with medians and interquartile range (IQR). Categorical data will be reported as 
counts and percentages. Missing data will be handled by multiple imputation, using 5 
imputations and 10 iterations unless the missingness exceeds 30% and/or missing at random 
cannot be assumed. Fully conditioned specification method and regression estimation will 
be used. For variables where we are unable to use regression estimation due to 
computational difficulties, predictive mean matching will be used [15]. 
Description of study flow  
The flow of participants through the study will be reported according to the STROBE 
guidelines [16] with a flow chart (see figure 1). Reasons for dropout will be provided where 
known. Differences between responders and non-responders will be evaluated. 
Participant characteristics 
Baseline characteristics of included patients will be presented for the whole sample and the 
following subgroups: (1) low, (2) medium, and (3) high risk of persistent disabling back pain 
according to the StarT Back Screening tool (see table 2).  

SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
Primary analysis  
First, type and frequency of different healthcare resources will be calculated for each of the 
follow-up periods; from baseline to 3 months, 3 to 6 months, and 9 to 12 months and 
presented as shown in table 3.  
Next, costs will be estimated based on information presented in table 3 and unit costs of 
healthcare resources collected from national pricelists (see table 1). Costs related to back 
medication will be estimated based on medication type (not exact medication name) and 
frequency of use. Data on dosage is not available. All costs will be presented in Norwegian 
Krones (NOK) and euros (€) 2020. Costs of healthcare utilization will be described with 
median and interquartile range for each follow-up period and for the whole year as shown in 
table 4. If data is highly skewed, bias-corrected and accelerated bootstrapping will be 
considered to derive confidence intervals for cost estimates.  
Secondary analysis 
First, type and frequency of different healthcare resources will be described for the one-year 
of follow-up for the following subgroups: (1) low, (2) medium, and (3) high risk of persistent 
disabling back pain according to the StarT Back Screening tool and presented as shown in 
table 5.  
Next, Kruscall Wallis test will be used to determine whether there is a significant difference 
between the three subgroups with regards to; number of primary care consultations, 
number of patients using back medication, number of patients receiving imaging (X-ray, MRI, 
CT) and number of patients receiving secondary care (back operation, hospitalization, 
rehabilitation stay). For all analyses, p values of < 0.05 will be considered statistically 
significant.  
Sample size and statistical power consideration 
This study contains secondary analyses embedded in the BACE-N study. Details on sample 
size calculation are provided in the BACE-N protocol (ClinicalTrials.gov Identifier: 
NCT04261309). With a sample size of 450 participants within the BACE-N study, we assume 
to have expectable power to describe healthcare utilization and estimate associated costs 
[17].  
Sensitivity analyses 
To assess the robustness of the results, the following sensitivity analyses will be carried out 
for cost calculations related to the primary analysis:  
1. Complete case analysis (without using imputation for missing data)  
2. Without outliers (outliers will be identified with simple scatter plots by visual 
inspection) 
 
 

SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
6. Selection bias, information bias and confounding  
Selection bias:  
Because of limited resources and practical reasons related to recruitment from a broad 
network of clinicians, the BACE-N lacks information on eligible study participants that 
declined to participate or for other reasons were not invited. Therefore, in order to assess 
representativeness, the BACE-N study sample will be compared on key sociodemographic 
variables with a sample from the longitudinal population study of people in the second half 
of life; The Norwegian study on life course, ageing and generation (NORLAG) [18]. The 
NORLAG study is expected to represent a representative sample of older people with 
musculoskeletal complaints. 
Response rate at each assessment point and reasons for loss to follow-up will be reported. 
Key baseline characteristics will be compared between those lost to follow-up and those 
remaining in the study.  
Information bias:  
To reduce the risk of information bias, the study outcome (costs) will be measured in an 
identical manner in all included cases, and in the best possible way within the framework of 
the BACE-N study.  
Covariates:  
Covariates may influence estimates of our primary outcome. Therefore, in line with the 
PROGRESS framework and recommendations for type 1 studies [19], we will describe 
potential covariates and the way they may alter our primary outcome. 
Potential covariates of costs related to healthcare utilization: gender [20-25], age [20, 22, 
26], educational level [27, 28], pain duration [21, 28-30], pain history [25], pain severity [22, 
25, 30, 31], radiating pain below the knee [29], disability [21, 23, 25, 28, 29], comorbidity 
[26, 32, 33], health-related quality of life [23], emotional well-being [21, 28, 29, 31, 32], 
kinesiophobia [29, 34], first healthcare provider [35] and costs related to healthcare 
utilization prior to inclusion.  
 
 
 
 
 
 
 
 
 

SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
Table 1 Cost categories, units, unit price, all numbers in Euros (€) and Norwegian kroner (NOK) for 2020 
Cost categories 
Unit 
Unit price (€) 
Unit price (NOK) 
Reference (source) 
Primary care  
 
 
 
 
General practitioner 
Per visit 
 
 
 
Physiotherapist 
Per visit 
 
 
 
Chiropractor 
Per visit 
 
 
 
Manuel therapist 
Per visit 
 
 
 
Naprapath 
Per visit 
 
 
 
Osteopath 
Per visit 
 
 
 
Psychologist 
Per visit 
 
 
 
Other therapists 
Per visit 
 
 
 
Back medication 
 
 
 
 
Paracetamol 
Per daily defined dose  
 
 
 
NSAID 
Per daily defined dose  
 
 
 
Muscle relaxant 
Per daily defined dose  
 
 
 
Sleep medication 
Per daily defined dose  
 
 
 
Cortisone 
Per daily defined dose  
 
 
 
Opioid 
Per daily defined dose  
 
 
 
Others 
Per daily defined dose  
 
 
 
Examinations  
 
 
 
 
Blood sample 
Per examination  
 
 
 
X-ray 
Per examination 
 
 
 
MRI 
Per examination 
 
 
 
CT 
Per examination 
 
 
 
Others?? 
Per examination 
 
 
 
Secondary care 
 
 
 
 
Back operation 
Per operation 
 
 
 
Hospitalization (non-operation) 
Per day 
 
 
 
Rehabilitation stay 
Per day 
 
 
 
NoMA, Norwegian Medicines Agency  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
Table 2 Patient characteristics and clinical status at baseline (n = X) 
 
 
 
  
Stratified risk profile* 
 
All participants 
(n = x) 
Low 
(n = x) 
Medium  
(n = x) 
High  
(n = x) 
Male, N (%) 
 
 
 
 
Age in years, mean (SD) 
 
 
 
 
Educational level high, N (%) 
 
 
 
 
Ethnicity Norwegian, N (%) 
 
 
 
 
First healthcare provider, N (%)  
 
 
 
 
  General practitioner  
 
 
 
 
  Physiotherapist 
 
 
 
 
  Chiropractor  
 
 
 
 
Pain location, N (%)  
 
 
 
 
  Lumbar 
 
 
 
 
  Thoracic 
 
 
 
 
  Radiating pain below the knee 
 
 
 
 
Pain severity last week (NRS 0-10), median (IQR) 
 
 
 
 
Pain duration, N (%) 
 
 
 
 
  < 6 weeks 
 
 
 
 
  6 weeks to 3 months 
 
 
 
 
  > 3 months  
 
 
 
 
Previous episodes of back pain, N (%) 
 
 
 
 
Disability (RMDQ 0-24), mean (SD) 
 
 
 
 
Comorbidity (SCQ, 0-15) 
 
 
 
 
Health-related QOL (SF36, 0-100), mean (SD) 
 
 
 
 
  Physical component 
 
 
 
 
  Mental component  
 
 
 
 
Emotional well-being (CES-D 0-60) 
 
 
 
 
Kinesiophobia (FABQ-PA 0-24) 
 
 
 
 
Healthcare utilization prior to inclusion   
 
 
 
 
Patients with primary care consultation last 6 weeks, N (%) 
 
 
 
 
  General practitioner 
 
 
 
 
  Physiotherapist 
 
 
 
 
  Chiropractor 
 
 
 
 
  Manual therapist 
 
 
 
 
  Naprapath  
 
 
 
 
  Osteopath 
 
 
 
 
  Psychologist 
 
 
 
 
  Other therapists 
 
 
 
 
Patients with use of back medication, N (%)? 
 
 
 
 
Patients with additional diagnostic examination last 6 months, N (%) 
 
 
 
 
  Blood sample 
 
 
 
 
  X-ray 
 
 
 
 
  MRI 
 
 
 
 
  CT 
 
 
 
 
Patients with previous hospitalization, N (%)  
 
 
 
 
Patients with previous rehabilitation stay, N (%) 
 
 
 
 
* According to the StarT Back Screening Tool 
 
 
 
 
 
 
 
 

SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
Table 3 Healthcare utilization throughout one-year of follow-up 
 
0-3 
months 
>3-6 
months 
>9-12 
months 
Primary care  
 
 
 
Patients with primary care consultation, N (%) 
 
 
 
  General practitioner 
 
 
 
  Physiotherapist 
 
 
 
  Chiropractor 
 
 
 
  Manual therapist 
 
 
 
  Naprapath  
 
 
 
  Osteopath 
 
 
 
  Psychologist 
 
 
 
  Other therapists 
 
 
 
Number of general practitioner consultations, median (IQR) 
 
 
 
Number of physiotherapist consultations, median (IQR) 
 
 
 
Number of chiropractor consultations, median (IQR) 
 
 
 
Number of manual therapist consultations, median (IQR) 
 
 
 
Number of naprapath consultations, median (IQR) 
 
 
 
Number of psychologist consultations, median (IQR) 
 
 
 
Number of other consultations, median (IQR) 
 
 
 
Back medication  
 
 
 
Patients with use of back medication, N (%) 
 
 
 
  Paracetamol 
 
 
 
  NSAID 
 
 
 
  Muscle relaxants 
 
 
 
  Sleep medication 
 
 
 
  Cortisone 
 
 
 
  Opioid 
 
 
 
  Others 
 
 
 
Frequency of use paracetamol, N (%) 
 
 
 
  Daily 
 
 
 
  Weekly 
 
 
 
  Monthly or less 
 
 
 
Frequency of use NSAID, cortisone N (%) 
 
 
 
  Daily 
 
 
 
  Weekly 
 
 
 
  Monthly or less 
 
 
 
Frequency of use muscle relaxants, sleep medication, N (%) 
 
 
 
  Daily 
 
 
 
  Weekly 
 
 
 
  Monthly or less 
 
 
 
Examinations 
 
 
 
Patients with additional diagnostic examination, N (%) 
 
 
 
  Blood sample 
 
 
 
  X-ray 
 
 
 
  MRI 
 
 
 
  CT 
 
 
 
Secondary care 
 
 
 
Patients with back operation, N (%) 
 
 
 
Patients with hospitalization, N (%)  
 
 
 
  Duration of stay in days, median (IQR) 
 
 
 
Patients with rehabilitation stay, N (%) 
 
 
 
  Duration of stay in days, median (IQR) 
 
 
 
 

SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
Table 4 Cost due to healthcare utilization from 0-3 month, >3-6 months, >9-12 months and the entire 
follow-up period (0-12 month)  
 
0-3 
months 
3-6 
months 
>9-12 
months 
0-12 
months* 
Primary care  
 
 
 
 
General practitioner 
 
 
 
 
Physiotherapist 
 
 
 
 
Chiropractor 
 
 
 
 
Manual therapist 
 
 
 
 
Naprapath  
 
 
 
 
Osteopath 
 
 
 
 
Psychologist 
 
 
 
 
Other therapists 
 
 
 
 
Back medication  
 
 
 
 
Paracetamol 
 
 
 
 
NSAID, cortisone 
 
 
 
 
Muscle relaxants, sleep medication 
 
 
 
 
Examinations 
 
 
 
 
Blood sample 
 
 
 
 
X-ray 
 
 
 
 
MRI 
 
 
 
 
CT 
 
 
 
 
Secondary care 
 
 
 
 
Back operation 
 
 
 
 
Hospitalization and/or rehabilitation stay  
 
 
 
 
Total costs  
 
 
 
 
Values are median (interquartile range) of costs (€). *Cost due to healthcare utilization for the entire follow-up 
period is calculated on the basis for the three follow-up periods 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
Table 5 Healthcare utilization throughout one-year of follow-up, across patients with 
different risk profile according to the StarT Back Screening tool 
 
Stratified risk profile 
 
Low 
(n = x) 
Medium  
(n = x) 
High  
(n = x) 
Primary care  
 
 
 
Patients with primary care consultation, N (%) 
 
 
 
Number of primary consultations, median (IQR) 
 
 
 
Back medication  
 
 
 
Patients with use of back medication, N (%) 
 
 
 
  Paracetamol 
 
 
 
  NSAID 
 
 
 
  Muscle relaxants 
 
 
 
  Sleep medication 
 
 
 
  Cortisone 
 
 
 
  Opioid 
 
 
 
Examinations 
 
 
 
Patients with additional diagnostic examination, N (%) 
 
 
 
  Blood sample 
 
 
 
  X-ray 
 
 
 
  MRI 
 
 
 
  CT 
 
 
 
Secondary care 
 
 
 
Patients with back operation, N (%) 
 
 
 
Patients with hospitalization, N (%)  
 
 
 
Patients with rehabilitation stay, N (%) 
 
 
 
 
 
Figure 1. Flow chart of the study   
 

SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
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SAP Healthcare utilization and related cost among older people seeking primary care due to back pain: a prospective cohort 
study with one year follow up (working title)  
 
 
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Hirsch, O., et al., Low back pain patient subgroups in primary care: pain characteristics, 
psychosocial determinants, and health care utilization. Clin J Pain, 2014. 30(12): p. 1023-32. 
32. 
Stewart, W.F., et al., Patterns of health care utilization for low back pain. J Pain Res, 2015. 8: 
p. 523-35. 
33. 
Ritzwoller, D.P., et al., The association of comorbidities, utilization and costs for patients 
identified with low back pain. BMC Musculoskelet Disord, 2006. 7: p. 72. 
34. 
Keeley, P., et al., Psychosocial predictors of health-related quality of life and health service 
utilisation in people with chronic low back pain. Pain, 2008. 135(1-2): p. 142-50. 
35. 
Fritz, J.M., J. Kim, and J. Dorius, Importance of the type of provider seen to begin health care 
for a new episode low back pain: associations with future utilization and costs. J Eval Clin 
Pract, 2016. 22(2): p. 247-52. 
 
 
 

 
 
 
 
Statistical analysis plan (SAP) for:  
Modifiable prognostic factors of high cost related to 
healthcare utilization among older people seeking primary 
care with a new episode of back pain - an identification and 
replication study (working title)   
 
 
Project:  
 
BACk pain in Elders in Norway (BACE-N) (BACE-N) 
NCT number:   
NCT04261309   
Document date: 
11.03.2021 
 
 
 
 
 
 
 
 
 
 
 
 
 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
1. Introduction to SAP  
1.1 Scope 
This document is a supplement to the BACE-N protocol (ClinicalTrials.gov Identifier: 
NCT04261309) and comprises a SAP for the article “Modifiable prognostic factors of high 
cost related to healthcare utilization among older people seeking primary care with a new 
episode of back pain - an identification and external validation study”. The current SAP has 
been written while data collection was ongoing (we had access to baseline data, but not to 
follow-up data) and it will be uploaded to the ClinicalTrials.gov before full access to the study 
database.  
2. Administrative information 
Version of SAP 
1.0 
Study sponsor 
Oslo Metropolitan University, the Norwegian Fund for Post-Graduate Training in 
Physiotherapy and “Et liv i bevegelse” (A life in movement) - Norwegian chiropractors’ 
research foundation 
Names, affiliations and roles of SAP contributors  
 
Name 
Title 
Affiliation 
Role  
Margreth Grotle 
Professor 
Oslo Metropolitan University 
P44, 0167 Oslo 
Phone: 90111172  
Email: mgrotle@oslomet.no 
Principal Investigator  
Rikke Munk Killingmo 
PhD-student 
Oslo Metropolitan University Main author of SAP  
Kjersti Storheim  
Professor 
Oslo Metropolitan University Contributor to SAP 
Daniëlle van der Windt Professor  
Keele University 
Contributor to SAP 
Bart Koes 
Professor 
Erasmus MC 
Contributor to SAP 
Sita Bierma-Zienstra 
Professor 
Erasmus MC 
Contributor to SAP 
Alessandro Chiarotto 
Post-doc, PhD Erasmus MC 
Contributor to SAP 
Milada C. Småstuen  
Professor 
Oslo Metropolitan University Statistical advisor  
Ørjan Nesse Vigdal 
PhD-student 
Oslo Metropolitan University Contributor to SAP 
Lise Kretz 
PhD-student 
Oslo Metropolitan University Contributor to SAP 
 
Signatures: Person writing SAP, senior statistician and chief investigator  
 
__________________________ __________________________ ________________________ 
Rikke Munk Killingmo 
Milada Småstuen 
Margreth Grotle 
Main author of the SAP 
Senior statistician responsible 
Principal Investigator 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
3. Study aim   
The aim of this study is 1) to identify modifiable prognostic factors for high costs related to 
healthcare utilization among older people seeking primary care with a new episode of back 
pain and 2) to replicate the identified associations of modifiable prognostic factors in a 
similar cohort of older back pain patients. 
4. Study design, population and method  
Study design  
This study will be carried out in two steps. First, modifiable prognostic factors will be 
identified in a prospective observational cohort study with one year of follow-up within a 
Norwegian primary care setting (the BACE-N). Next, a replication analysis of identified 
prognostic factors will be conducted in a prospective observational cohort study within a 
Dutch primary care setting (the BACE-D).  
The BACE-N and the BACE-D studies are part of the international BACE consortium [1]. The 
BACE-N study (ClinicalTrials.gov Identifier: NCT04261309) was classified as a quality 
assessment study by the Norwegian Regional Committee for medical Research Ethics 
(reference no. 2014/1634/REK vest) and approved by the Norwegian Social Science Data 
Service (reference no. 42149) in 2015. 
Study population and recruitment 
BACE-N: Eligible patients are people 55 years of age or older who seek primary care 
(physiotherapist, chiropractor or GP) with a new episode of back pain (preceded by 6 
months without visiting a primary care provider for similar complaints). Patients are 
excluded if they have difficulties completing the questionnaires (e.g. unable to speak, read 
or write in Norwegian) or if they have difficulties completing the physical examination (e.g. 
are wheelchair bound). Patients are recruited from physiotherapist, chiropractors and GPs 
working in Norwegian primary care between April 2015 and February 2020. Patients who 
meet the eligibility criteria and complete the consent to participate are included in the 
study.  
BACE-D: Eligible patients were people over 55 years of age (n=675) who sought primary care 
(GPs) with a new episode of back pain (preceded by 6 months without visiting a primary care 
provider for similar complaints). Patients were excluded if they had difficulties completing 
the questionnaires (e.g. unable to speak, read or write in Dutch) or if they had difficulties 
completing the physical examination (e.g. are wheelchair bound). Patients were recruited 
from GPs working in Dutch primary care between March 2009 and September 2011. Patients 
who meet the eligibility criteria and complete the consent to participate were included in 
the study.  
Method  
BACE-N: At baseline all patients responded to a comprehensive questionnaire and went 
through a standardized physical examination conducted by local research assistants at test 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
stations established within each recruiting area. Follow-up questionnaires will be sent at 3, 
6, and 12 months after inclusion for completion at home. All questionnaires are preferably 
completed electronically using the Infopad system, but paper versions will be available for 
patients not familiar with electronic data collection. All information will be stored and 
analysed securely through Service for sensitive data (TSD) at the University of Oslo, Norway.      
BACE-D: At baseline all patients responded to a comprehensive questionnaire and went 
through a standardized physical examination. Follow-up questionnaires were sent (by e-mail 
or postal) at 3, 6, 9 and 12 months after inclusion.  
Variables 
Outcome variable 
The outcome of this study is costs related to healthcare utilization aggregated for one year 
of follow up and dichotomized as high and low. Having high costs related to healthcare 
utilization is defined as patients with costs in the top 25th percentile [2, 3].  
Healthcare utilization within the BACE-N and the BACE-D will be self-reported and include; 
consultation to healthcare professionals (type and frequency), number of diagnostic 
examinations (type and frequency), number of days of hospitalization and/or 
institutionalisation (only included in the BACE-N), back operations and use of back 
medication (both prescription and over-the-counter, type and frequency). All variables, 
except back operations, will be reported with a 3 month recall period at 3, 6, and 12 months 
follow-up for the BACE-N, at 3, 6, 9, and 12 months follow-up for the BACE-D. Back 
operations will be reported with a 12 month recall period at 12 months follow-up.  
Healthcare utilization during the one year of follow-up will be described as shown in table 3 
(BACE-N and BACE-D). The total cost of healthcare utilization will be estimated based on 
information presented in table 3 and unit costs of healthcare resources collected from 
national pricelists in Norway and the Nederlands (see table 1). 
Potential modifiable prognostic factors  
Potential modifiable prognostic factors are factors expected to have the potential to be 
modified through healthcare system encounters and therefore classified as modifiable. 
Potential modifiable prognostic factors of high-costs related to healthcare utilization are 
based on previous literature and will be measured at baseline. 
• Pain severity [2-7]  measured by the NRS  
• Disability [2-6, 8] measured by the RMDQ 
• Health-related quality of life [6, 7] measured by the SF36 using the physical and 
mental summary score 
• Emotional well-being [2, 3, 8-10] measured by the CES-D 
• Kinesiophobia [3, 10] measured by the FABQ-PA 
• Comorbidity [11] measured by the SCQ 
• Radiating pain below the knee [3] measured by the question “did your back pain 
radiate to your legs last week? If yes, how far down did the pain radiate last week?” 
categorized into yes or no 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
• Expectations of recovery measured with a five-point scale  
The Numeric Rating Scale (NRS) will be used to measure average pain severity last week [12]. 
The NRS, scored from 0 (no pain) to 10 (maximum pain), has been widely used to evaluate 
pain and has proven to be preferable when examining low back pain patients [13], also for 
Norwegian patients [14].  
The Roland Morris Disability Questionnaire (RMDQ) [15] will be used to measure disability. 
The RMDQ is a widely used back-specific patient-reported measure of pain-related disability 
(0 = no disability, 24 = totally disabled). The Norwegian version has been validated and found 
to have good measurement properties when used among patients with low back pain [14, 
16].  
The Short Form-36 Health Status Questionnaire (SF36) [17] will be used to assess health-
related quality of life. The SF36 consist of 36 items. It measures health on eight multi-item 
dimensions, covering physical functioning, social functioning, role limitations (physical 
problems), role limitations (emotional problems), mental health, vitality, pain, and overall 
evaluation of health [17]. Data-completeness of the SF36 in the general population in 
Norway seems to strongly declined with increasing age [18]. Hence, caution should be 
exercised when assessing subjective health or employing the norms among subjects aged 70 
years or over [18]. 
The Center for Epidemiologic Studies Depression Scale (CES-D) will be used to assess 
emotional well-being. The CES-D has been widely used in studies of late-life depression. 
Psychometric properties are generally favourable [19]. The Norwegian version of the CES-D 
has been used among older patients in order to measure depression symptoms [20]. 
The Fear Avoidance Beliefs Questionnaire, physical activity subscale (FABQ-PA) [21] will be 
used to assess kinesiophobia. The FABQ-PA consists of four questions aimed towards 
physical activity, scored on a 7-point ordinal scale, which are summed up to a sum score, 
ranging from 0 (no fear) to 24 (maximum fear). The questionnaire has been translated into 
Norwegian and has shown acceptable psychometric properties in Norwegian patients with 
low back pain [22].  
The Self-Administered Comorbidity Questionnaire (SCQ) [23] will be used to assess 
comorbidity. The SCQ is a 14-item measure of comorbidity for clinical and health services 
research settings. An individual can receive a maximum of 3 points for each medical 
condition: 1 point for the presence of the problem, another point if he/she receives 
treatment for it, and an additional point if the problem causes a limitation in functioning. 
Because there are 12 defined medical problems and 3 optional conditions, the maximum 
score totals 45 points if the open‐ended items are used and 36 points if only the close‐ended 
items are used.  
Potential covariates 
Potential covariates will be included in the analyses based on previous literature and will be 
measured at baseline.  
• Sex [4-6, 24, 25] 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
• Age [4, 6, 24, 25] 
• Education level [8, 26] measured as the highest education completed, categorised 
into high vs low (low consists of up to high school and occupational high school)  
• Employment status measured by the question “do you have a paying job?” 
categorized into yes or no 
• Pain duration [2] measured by the question “how many days have you had your 
current back pain?” 
• Pain history [5] measured by the question “have you had back pain before?” 
categorized into yes or no 
• First healthcare provider [27]  
• Costs related to healthcare utilization prior to inclusion 
Healthcare utilization prior to inclusion will be measured (with variables as described above) 
at baseline, in the period from baseline to 6 and 12 weeks retrospectively, for the BACE-N 
and the BACE-D study respectively. The total cost of healthcare utilization will be estimated 
as described above.  
Other variables 
Included patients (BACE-N and BACE-D) will be described with respect to the following 
baseline characteristics: ethnicity, pain location and healthcare utilization prior to inclusion 
(see table 2). In addition, we have included the following potential prognostic factors and 
covariates (as described above): age, gender, educational level, employment status, first 
healthcare provider, pain severity, pain duration, pain history, disability, health-related 
quality of life, emotional well-being, kinesiophobia, expectation of recovery and 
comorbidity.  
5. Statistical analyses 
General analysis considerations  
All analyses described in this plan are considered a priori in that they have been defined in 
the protocol and/or in this SAP. All post hoc analyses will be identified as such in the article if 
relevant. All analyses will be carried out by a PhD-student using SPSS version 26 and 
controlled by a senior researcher/statistician. All statistical tests will be two-sided, and 
nominal p-values will be reported. All confidence intervals will be reported as 95%. 
Preliminary analyses assessing the influence of missing data and assumptions of normality 
for continuous variables will be conducted. The assumption of normal distribution will be 
investigated using histograms and QQ-plots. Normally distributed data will be presented 
with means and standard deviations (SDs), skewed data with medians and interquartile 
range (IQR). Categorical data will be reported as counts and percentages. Missing data will 
be handled by multiple imputation, using 5 imputations and 10 iterations unless the 
missingness exceeds 30% and missing at random cannot be assumed. Fully conditioned 
specification method and regression estimation will be used. For variables where we are 
unable to use regression estimation due to computational difficulties, predictive mean 
matching will be used [28]. 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
Description of study flow  
The flow of participants through the study will be reported according to the STROBE 
guidelines [29] with a flow chart (see figure 1). Reasons for dropout will be provided where 
known. Differences between responders and non-responders will be evaluated. 
Participant characteristics 
Baseline characteristics of included patients will be presented as shown in table 2.  
Preparatory analysis  
First, type and frequency of use of different healthcare resources will be calculated for each 
of the follow-up periods; from baseline to 3 months, 3 to 6 months, and 9 to 12 months for 
the BACE-N study, and from baseline to 3 months, 3 to 6 months, 6 to 9 months, and 9 to 12 
months for the BACE-D. Healthcare utilization will be presented as shown in table 3.  
Next, costs will be estimated based on information presented in table 3, and unit costs of 
healthcare resources collected from national pricelists in Norway and the Nederlands (see 
table 1). Costs related to back medication will be estimated based on medication type (not 
exact medication name) and frequency of use. Data on dosage is not available. All costs will 
be presented in Euros (€) 2020. Costs of healthcare utilization will be described with median 
and interquartile range for the entire follow-up period as shown in table 4.  
Identification analysis  
Univariable and multivariable binary logistic regression models will be used to investigate 
individual association (crude and adjusted for selected covariates) between each predefined 
prognostic factor and costs related to healthcare utilization (within the BACE-N). The cost 
score will be entered into the model as a dependent dichotomous variable (high cost defined 
as patients with cost in the top 25th percentile, yes/no). The results will be presented as 
crude and adjusted odds ratios (OR) with 95% confidence intervals (CI) as shown in table 5.  
Replication analysis  
Univariable and multivariable binary logistic regression models will be used, as described 
above, to replicate findings from the identification analysis within the BACE-D material. The 
results will be presented as crude and adjusted odds ratios (OR) with 95% confidence 
intervals (CI) as shown in table 5. The decision on whether findings are replicated will be 
based on the size and direction of the association, the confidence interval and the p-value 
for each of the predefined prognostic factors [30].  
Sample size  
This study contains secondary analyses embedded in the BACE-N and the BACE-D study. 
Details on sample size calculation are provided in the BACE-N (ClinicalTrials.gov Identifier: 
NCT04261309) and the BACE-D protocol [1].  
To determine statistical power of this study we used number of events per variable (EPV) [46-50] and 
the rule-of-thumb of “10 events per 1 analysed variable” [51-54]. With a sample size of 450 
participants within the BACE-N study, we anticipate 112 participants to be in the top 25th percentile 
of costs due to healthcare utilization and categorised as having high costs (yes/no) due to healthcare 
utilization (events). An EPV of 10 will allow a maximum of 11 prognostic variables to be included in 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
the final multivariable prediction model. With a sample size of 675 participants in the BACE-D, we 
anticipate 168 participants to be in the top 25th percentile of costs due to healthcare utilization and 
defined as having high costs (yes/no) due to healthcare utilization (events). An EPV of 10 will allow a 
maximum of 16 prognostic variables to be included in the final multiple prediction model.  
Sensitivity analysis 
To assess the robustness of the results complete case analysis (without using imputation for 
missing data) will be carried out as a sensitivity analysis.  
6. Selection bias, information bias and covariates  
Selection bias:  
Because of limited resources and practical reasons related to recruitment from a broad 
network of clinicians, the BACE-N and the BACE-D lacks information on eligible study 
participants that declined to participate or for other reasons were not invited. In order to 
assess representativeness, the BACE-N study sample will be compared on key 
sociodemographic variables with a sample from the longitudinal population study of people 
in the second half of life; The Norwegian study on life course, ageing and generation 
(NORLAG). The NORLAG study is expected to represent a representative sample of older 
people with musculoskeletal complaints.  
Response rate at each assessment point and reasons for loss to follow-up will be reported. 
Key baseline characteristics will be compared between those lost to follow-up and those 
remaining in the study.  
Information bias:  
To reduce the risk of information bias, the study outcome (costs) will be measured in an 
identical manner in all included cases, and in the best possible way within the framework of 
the BACE-N and the BACE-D study.  
Covariates: 
Covariates may influence associations between prognostic factors and outcome. Therefore, 
in line with the PROGRESS framework and recommendations for type 2 studies [31], we will 
adjust for covariates when evaluating prognostic factors. 
 
 
 
 
 
 
 
 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
Table 1 Cost categories, units, unit price, all numbers in Euros (€) for 2020? 
Cost categories 
Unit 
Norwegian  
unit price (€) 
Dutch unit 
price (€) 
Reference (source) 
Primary care  
 
 
 
 
General practitioner 
Per visit 
 
 
 
Medical specialist  
Per visit 
 
 
 
Occupational 
physician 
Per visit 
 
 
 
Physiotherapist 
Per visit 
 
 
 
Chiropractor 
Per visit 
 
 
 
Manuel therapist 
Per visit 
 
 
 
Naprapath 
Per visit 
 
 
 
Osteopath 
Per visit 
 
 
 
Psychologist 
Per visit 
 
 
 
Other therapists 
Per visit 
 
 
 
Back medication 
 
 
 
 
Paracetamol 
Per daily 
defined dose  
 
 
 
NSAID 
Per daily 
defined dose  
 
 
 
Muscle relaxant 
Per daily 
defined dose  
 
 
 
Sleep medication 
Per daily 
defined dose  
 
 
 
Cortisone 
Per daily 
defined dose  
 
 
 
Opioid 
Per daily 
defined dose  
 
 
 
Antidepressant 
Per daily 
defined dose 
 
 
 
Anticonvulsant  
Per daily 
defined dose 
 
 
 
Others 
Per daily 
defined dose  
 
 
 
Examinations  
 
 
 
 
Blood sample 
Per 
examination  
 
 
 
X-ray 
Per 
examination 
 
 
 
MRI 
Per 
examination 
 
 
 
CT 
Per 
examination 
 
 
 
Others?? 
Per 
examination 
 
 
 
Secondary care 
 
 
 
 
Back operation 
Per operation 
 
 
 
Hospitalization 
(non-operation) 
Per day 
 
 
 
Rehabilitation stay 
Per day 
 
 
 
NoMA, Norwegian Medicines Agency 
 
 
 
 
 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
Table 2 Patient characteristics and clinical status at baseline 
 
 
 
BACE-N 
(n = x) 
BACE-D 
(n = x) 
Female, N (%) 
 
 
Age in years, mean (SD) 
 
 
Education level high, N (%) 
 
 
Ethnicity Norwegian (BACE-N) or Dutch (BACE-D), N (%) 
 
 
Employment status, N (%) 
 
 
  Currently paid work 
 
 
First healthcare provider, N (%)  
 
 
  General practitioner  
 
 
  Physiotherapist 
 
 
  Chiropractor  
 
 
Pain location, N (%)  
 
 
  Lumbar 
 
 
  Thoracic 
 
 
  Radiating pain below the knee 
 
 
Average pain severity last week (NRS 0-10), median (IQR) 
 
 
Pain duration, N (%) 
 
 
  < 6 weeks 
 
 
  6 weeks to 3 months 
 
 
  > 3 months  
 
 
Previous episodes of back pain, N (%) 
 
 
Disability (RMDQ 0-24), mean (SD) 
 
 
Comorbidity (SCQ, 0-15) 
 
 
Health-related QOL (SF36, 0-100), mean (SD) 
 
 
  Physical component 
 
 
  Mental component  
 
 
Emotional well-being (CES-D 0-60) 
 
 
Kinesiophobia (FABQ-PA 0-24) 
 
 
Expectation of recovery within 3 months, N (%)  
 
 
  Fully recovered 
 
 
  Much better 
 
 
  No change or worse  
 
 
Healthcare utilization prior to inclusion   
 
 
Patients with primary care consultation last 6 (BACE-N) or 12 (BACE-D) weeks, N (%) 
 
 
  General practitioner 
 
 
  Medical specialist 
 
 
  Occupational physician  
 
 
  Physiotherapist 
 
 
  Chiropractor 
 
 
  Manual therapist 
 
 
  Naprapath  
 
 
  Psychologist 
 
 
  Other therapists 
 
 
Patients with use of back medication, N (%)? 
 
 
Patients with diagnostic examination last 6 (BACE-N) or 3 (BACE-D) months, N (%) 
 
 
  Blood sample 
 
 
  X-ray 
 
 
  MRI/CT scan 
 
 
Patients with previous hospitalization, N (%)  
 
 
Patients with previous rehabilitation stay, N (%) 
 
 
NRS indicates Numeric Rating Scale; RMDQ, The Roland Morris Disability Questionnaire; SCQ, The Self-Administered Comorbidity Questionnaire; 
SF-36, 36-Item Short-Form Health Survey; CES-D, The Center for Epidemiologic Studies Depression Scale; FABQ-PA, The Fear Avoidance Beliefs 
Questionnaire, physical activity subscale.  

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
Table 3 Healthcare utilization throughout one-year of follow-up 
 
 
 
 
 
BACE-N 
BACE-D 
 
0-3 
months 
>3-6 
months 
>9-12 
months 
0-3 
months 
>3-6 
months 
>6-9 
months 
>9-12 
months 
Primary care  
 
 
 
 
 
 
 
Patients with primary care consultation, N (%) 
 
 
 
 
 
 
 
  General practitioner 
 
 
 
 
 
 
 
  Medical specialist 
 
 
 
 
 
 
 
  Occupational physician  
 
 
 
 
 
 
 
  Physiotherapist 
 
 
 
 
 
 
 
  Chiropractor 
 
 
 
 
 
 
 
  Manual therapist 
 
 
 
 
 
 
 
  Naprapath  
 
 
 
 
 
 
 
  Psychologist 
 
 
 
 
 
 
 
  Other therapists 
 
 
 
 
 
 
 
No. of general practitioner consultations, median (IQR) 
 
 
 
 
 
 
 
No. of medical specialist consultations, median (IQR) 
 
 
 
 
 
 
 
No. of occupational physician consultations, median (IQR) 
 
 
 
 
 
 
 
No. of physiotherapist consultations, median (IQR) 
 
 
 
 
 
 
 
No. of chiropractor consultations, median (IQR) 
 
 
 
 
 
 
 
No. of manual therapist consultations, median (IQR) 
 
 
 
 
 
 
 
No. of naprapath consultations, median (IQR) 
 
 
 
 
 
 
 
No. of psychologist consultations, median (IQR) 
 
 
 
 
 
 
 
No. of other consultations, median (IQR) 
 
 
 
 
 
 
 
Back medication  
 
 
 
 
 
 
 
Patients with use of back medication, N (%) 
 
 
 
 
 
 
 
  Paracetamol 
 
 
 
 
 
 
 
  NSAID 
 
 
 
 
 
 
 
  Muscle relaxants 
 
 
 
 
 
 
 
  Sleep medication 
 
 
 
 
 
 
 
  Cortisone 
 
 
 
 
 
 
 
  Opioid 
 
 
 
 
 
 
 
  Others 
 
 
 
 
 
 
 
Frequency of use paracetamol, N (%) 
 
 
 
 
 
 
 
  Daily 
 
 
 
 
 
 
 
  Weekly 
 
 
 
 
 
 
 
  Monthly or less 
 
 
 
 
 
 
 
Frequency of use NSAID, cortisone N (%) 
 
 
 
 
 
 
 
  Daily 
 
 
 
 
 
 
 
  Weekly 
 
 
 
 
 
 
 
  Monthly or less 
 
 
 
 
 
 
 
Frequency of use muscle relaxants, sleep medication, N (%) 
 
 
 
 
 
 
 
  Daily 
 
 
 
 
 
 
 
  Weekly 
 
 
 
 
 
 
 
  Monthly or less 
 
 
 
 
 
 
 
Examinations 
 
 
 
 
 
 
 
Patients with additional diagnostic examination, N (%) 
 
 
 
 
 
 
 
  Blood sample 
 
 
 
 
 
 
 
  X-ray 
 
 
 
 
 
 
 
  MRI/CT scan 
 
 
 
 
 
 
 
  Others 
 
 
 
 
 
 
 
Secondary care 
 
 
 
 
 
 
 
Patients with back operation, N (%) 
 
 
 
 
 
 
 
Patients with hospitalization, N (%)  
 
 
 
 
 
 
 
  Duration of stay in days, median (IQR) 
 
 
 
 
 
 
 
Patients with rehabilitation stay, N (%) 
 
 
 
 
 
 
 
  Duration of stay in days, median (IQR) 
 
 
 
 
 
 
 
 
 
 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
Table 4 Cost related to healthcare utilization from 0-12 month*  
 
 
 
BACE-N 
BACE-D 
Primary care  
 
 
 
 
General practitioner 
 
 
 
 
Medical specialist 
 
 
 
 
Occupational physician  
 
 
 
 
Physiotherapist 
 
 
 
 
Chiropractor 
 
 
 
 
Manual therapist 
 
 
 
 
Naprapath  
 
 
 
 
Psychologist 
 
 
 
 
Other therapists 
 
 
 
 
Back medication  
 
 
 
 
Paracetamol 
 
 
 
 
NSAID, cortisone 
 
 
 
 
Muscle relaxants, sleep medication 
 
 
 
 
Examinations 
 
 
 
 
Blood sample 
 
 
 
 
X-ray 
 
 
 
 
MRI 
 
 
 
 
CT 
 
 
 
 
Others 
 
 
 
 
Secondary care 
 
 
 
 
Back operation 
 
 
 
 
Hospitalization and/or rehabilitation stay  
 
 
 
 
Total costs  
 
 
 
 
Values are median (interquartile range) of costs (€). *Cost related to healthcare utilization for the entire follow-up period is 
calculated on basis of the three (BACE-N) and four (BACE-D) follow-up periods 
 
 
Table 5 Binary logistic regression analyses; individual associations between modifiable prognostic factors and high costs 
related to healthcare utilization (dependent variable)  
 
BACE-N 
BACE-D 
 
Crude OR (95% CI) 
Adjusted OR* (95% CI) 
Crude OR (95% CI) 
Adjusted OR* (95% CI) 
Pain severity (NRS, 0-10) 
 
 
 
 
Disability (RMDQ, 0-24) 
 
 
 
 
Health-related QOL (SF36, 0-100) 
 
 
 
 
  Physical component 
 
 
 
 
  Mental component  
 
 
 
 
Emotional well-being (CES-D 0-60) 
 
 
 
 
Kinesiophobia (FABQ-PA 0-24) 
 
 
 
 
Comorbidity (SCQ, 0-15) 
 
 
 
 
Radiating pain below the knee 
  Yes 
  No 
 
 
 
 
Expectation of recovery within 3 
months, N (%) 
 
 
 
 
OR indicates odds ratio; CI, confidence interval; NRS, Numeric Rating Scale; RMDQ, The Roland Morris Disability Questionnaire; SF-36, 36-Item Short-Form 
Health Survey; CES-D, The Center for Epidemiologic Studies Depression Scale; FABQ-PA, The Fear Avoidance Beliefs Questionnaire, physical activity 
subscale; SCQ, The Self-Administered Comorbidity Questionnaire. *Adjusted by gender, age, education level, employment status, pain duration, pain 
history and costs related to healthcare utilization prior to inclusion. 
 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
 
Figure 1. Flow chart of the study   
 
 
 
 
 
 
 
 
 
 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
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Becker, A., et al., Low back pain in primary care: costs of care and prediction of future health 
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7. 
Lentz, T.A., et al., Factors associated with persistently high-cost health care utilization for 
musculoskeletal pain. PLoS One, 2019. 14(11): p. e0225125. 
8. 
Lim, K.L., P. Jacobs, and S. Klarenbach, A population-based analysis of healthcare utilization 
of persons with back disorders: results from the Canadian Community Health Survey 2000-
2001. Spine (Phila Pa 1976), 2006. 31(2): p. 212-8. 
9. 
Stewart, W.F., et al., Patterns of health care utilization for low back pain. J Pain Res, 2015. 8: 
p. 523-35. 
10. 
Keeley, P., et al., Psychosocial predictors of health-related quality of life and health service 
utilisation in people with chronic low back pain. Pain, 2008. 135(1-2): p. 142-50. 
11. 
Ritzwoller, D.P., et al., The association of comorbidities, utilization and costs for patients 
identified with low back pain. BMC Musculoskelet Disord, 2006. 7: p. 72. 
12. 
Von Korff, M., M.P. Jensen, and P. Karoly, Assessing global pain severity by self-report in 
clinical and health services research. Spine (Phila Pa 1976), 2000. 25(24): p. 3140-51. 
13. 
Strong, J., R. Ashton, and D. Chant, Pain intensity measurement in chronic low back pain. Clin 
J Pain, 1991. 7(3): p. 209-18. 
14. 
Grotle, M., J.I. Brox, and N.K. Vollestad, Concurrent comparison of responsiveness in pain and 
functional status measurements used for patients with low back pain. Spine (Phila Pa 1976), 
2004. 29(21): p. E492-501. 
15. 
Roland, M. and R. Morris, A study of the natural history of back pain. Part I: development of a 
reliable and sensitive measure of disability in low-back pain. Spine (Phila Pa 1976), 1983. 8(2): 
p. 141-4. 
16. 
Grotle, M., J.I. Brox, and N.K. Vollestad, Cross-cultural adaptation of the Norwegian versions 
of the Roland-Morris Disability Questionnaire and the Oswestry Disability Index. J Rehabil 
Med, 2003. 35(5): p. 241-7. 
17. 
Ware, J.E., Jr. and C.D. Sherbourne, The MOS 36-item short-form health survey (SF-36). I. 
Conceptual framework and item selection. Med Care, 1992. 30(6): p. 473-83. 
18. 
Loge, J.H. and S. Kaasa, Short form 36 (SF-36) health survey: normative data from the general 
Norwegian population. Scand J Soc Med, 1998. 26(4): p. 250-8. 
19. 
Radloff, L.S., The CES-D scale: a Self-Report Depression Scale for Research in the General 
Population. Applied psychological measurement, 1977. (1(3)): p. 385-355. 
20. 
SE., T.K.C., Funksjonshemning, ensomhet og depresjon: Hva betyr ensomhet for om personer 
med funksjonshemning opplever depresjon? Tidsskrift Norsk Psykologforening, 2008. 1. 
21. 
Waddell, G., et al., A Fear-Avoidance Beliefs Questionnaire (FABQ) and the role of fear-
avoidance beliefs in chronic low back pain and disability. Pain, 1993. 52(2): p. 157-68. 
22. 
Grotle, M., J.I. Brox, and N.K. Vollestad, Reliability, validity and responsiveness of the fear-
avoidance beliefs questionnaire: methodological aspects of the Norwegian version. J Rehabil 
Med, 2006. 38(6): p. 346-53. 

SAP Modifiable prognostic factors of high cost related to healthcare utilization among older people seeking 
primary care with a new episode of back pain - an identification and replication study (working title)   
 
23. 
Sangha, O., et al., The Self-Administered Comorbidity Questionnaire: a new method to assess 
comorbidity for clinical and health services research. Arthritis Rheum, 2003. 49(2): p. 156-63. 
24. 
Rattay, P., et al., [Utilization of outpatient and inpatient health services in Germany: results of 
the German Health Interview and Examination Survey for Adults (DEGS1)]. 
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz, 2013. 56(5-6): p. 832-44. 
25. 
Chechulin, Y., et al., Predicting patients with high risk of becoming high-cost healthcare users 
in Ontario (Canada). Healthc Policy, 2014. 9(3): p. 68-79. 
26. 
Hoebel, J., et al., Socioeconomic Status and Use of Outpatient Medical Care: The Case of 
Germany. PLoS One, 2016. 11(5): p. e0155982. 
27. 
Fritz, J.M., J. Kim, and J. Dorius, Importance of the type of provider seen to begin health care 
for a new episode low back pain: associations with future utilization and costs. J Eval Clin 
Pract, 2016. 22(2): p. 247-52. 
28. 
Van Buuren, S., Flexible imputation of missing data. 2018: CRC press. 
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von Elm, E., et al., The Strengthening the Reporting of Observational Studies in Epidemiology 
(STROBE) statement: guidelines for reporting observational studies. PLoS Med, 2007. 4(10): p. 
e296. 
30. 
Kent, P., et al., A conceptual framework for prognostic research. BMC Med Res Methodol, 
2020. 20(1): p. 172. 
31. 
Riley, R.D., et al., Prognosis Research Strategy (PROGRESS) 2: prognostic factor research. PLoS 
Med, 2013. 10(2): p. e1001380. 
 

 
 
Statistical analysis plan (SAP) for:  
One-year clinical course of back-related disability and prognostic 
value of comorbidity on disability during one year follow-up in older 
patients visiting primary care with a new episode of back pain 
 
 
 
Project:   
BACk pain in Elders in Norway (BACE-N): a prospective cohort study of older people visiting primary 
care with a new episode of back pain 
 
NCT identifier: NCT04261309 
Document date: 22.03.2021 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
Table of Contents 
Authors: ................................................................................................. Error! Bookmark not defined. 
Project: ................................................................................................................................................ 1 
Administrative information ..................................................................................................................... 3 
Scope ................................................................................................................................................... 3 
Working title........................................................................................................................................ 3 
Version of SAP .................................................................................................................................... 3 
Ethical approval ................................................................................................................................... 3 
Names, affiliations and roles of SAP contributors .............................................................................. 3 
Study sponsor ...................................................................................................................................... 4 
Signatures ............................................................................................................................................ 4 
Introduction ............................................................................................................................................. 5 
Background and rationale for study .................................................................................................... 5 
Study design and method ........................................................................................................................ 5 
Study design ........................................................................................................................................ 5 
Study population.................................................................................................................................. 5 
Data collection ..................................................................................................................................... 6 
Variables .............................................................................................................................................. 6 
Statistical power consideration ............................................................................................................ 7 
Statistical analyses ................................................................................................................................... 8 
General statistical considerations ........................................................................................................ 8 
Statistical analyses ............................................................................................................................... 8 
Handling of missing data ..................................................................................................................... 7 
Sensitivity analyses ............................................................................................................................. 9 
Proposed tables and figures ................................................................................................................... 10 
References ............................................................................................................................................. 15 
 
 
 

Administrative information 
Scope 
This document is a supplement to the BACE-N protocol (ClinicalTrials.gov Identifier: NCT04261309) 
The current Statistical Analysis Plan has been written while data collection was ongoing (we had 
access to baseline data, but not to follow-up data) and it will be uploaded to the ClinicalTrials.gov 
before full access to the study database.  
 
Working title 
One-year clinical course of back-related disability and prognostic value of comorbidity on 
disability during one year follow-up in older patients visiting primary care with a new episode 
of back pain 
 
 
Version of SAP 
1.0 
Ethical approval 
The BACE-N study was deemed a “quality control project” by the Regional Ethical committee, and 
treatment by the ethical committee was thus not considered necessary as per 11.11.2014 (reference 
number: 2014/1634/REK vest) 
 
Approval from the Norwegian Social Science Data Service was obtained on 02.03.2015 (reference 
number: 42149).  
 
 
Names, affiliations and roles of SAP contributors 
Name 
Title 
Affiliation 
Role  
Margreth Grotle 
PT, professor, PhD Oslo Metropolitan 
University 
P44, 0167 Oslo 
Phone: 90111172  
Email: mgrotle@oslomet.no 
Principal 
Investigator  
Ørjan Nesse Vigdal 
PT, PhD-student 
Oslo Metropolitan 
University 
Main author of SAP 
Rikke Munk 
Killingmo 
PT, PhD-student 
Oslo Metropolitan 
University 
Contributor to SAP  
Kjersti Storheim  
PT, professor, PhD Oslo Metropolitan 
University 
Contributor to SAP 
Milada C. Småstuen  
Professor, PhD 
Oslo Metropolitan 
University 
Statistical advisor  
Lise Kretz 
Chiro, PhD-
student 
Oslo Metropolitan 
University 
Contributor to SAP 
 

Study sponsor 
The BACE-N-study has received funding from Oslo Metropolitan University, The Norwegian Fund 
for Post-Graduate Training in Physiotherapy and “Et liv i bevegelse” (A life in movement) – 
Norwegian chiropractors’ research foundation. 
 
Signatures 
 
 
 
______________________                ________________________             _______________________ 
Ørjan Nesse Vigdal                      Milada C. Småstuen                      Margreth Grotle 
Main author of SAP                     Statistical advisor                          Principal investigator 
 
 
 
 
 

Introduction 
Background and rationale for study 
Back pain is common in all age groups (1), and one systematic review highlights that 
disabling back pain is more prevalent in older people than in younger people (2). The clinical 
course of back-related disability in older adults with back pain has not been extensively 
studied. Two studies suggests that improvements in disability were modest the first three 
months, with little to no improvements on group level after three months (3, 4). It is well 
documented that number of comorbidities are associated with the clinical course of back-
related disability in older adults, but the prognostic value of comorbidity is still highly 
uncertain (5-11). 
 
Study aim: 
The primary aim of this study is to examine the clinical course of back-related disability 
measured at baseline, 3, 6 and 12 months after a new episode of back pain. The secondary aim 
is to assess the prognostic value of number and severity of comorbidity at baseline for 
changes in back-related disability over one year of follow-up.  
 
 
Study design and method 
Study design 
BACE-N is based on the previously published BACE study protocol from the BACE 
international consortium (12). The BACE-N protocol has been published (ClinicalTrials.gov 
Identifier: NCT04261309).  
 
The BACE-N study is a prospective observational cohort study with a 2-year follow-up time. 
This study will use data from baseline, 3, 6 and 12 months of follow-up. Design of the study 
was made within the framework PROGnosis RESearch Strategy (PROGRESS), which is a 
framework for ensuring and enhancing the quality of prognostic studies (13). The primary aim 
is relating overall prognosis, and the secondary aim is relating confirmatory prognostic factor 
research (13, 14).  
 
Study population 
Patients aged ≥55 years visiting a general practitioner (GP), physiotherapist or chiropractor 
for a new episode of back pain are invited to participate in the study. Back pain is defined as 
pain located in the region from the top of the scapula to the sacrum, with or without radiating 
leg pain. An episode of back pain is defined as “new” if the patient has not received health 
care for the same back complaint during the last 6 months. The exclusion criteria were: 
Difficulty completing the study questionnaires due to language or cognitive difficulties, 
mobility impairments impeding the clinical examination (wheelchair-bound patients), had 
received healthcare for the same back complaint during the last 6 months (notwithstanding 
care initiated within the previous 4 weeks from time of baseline assessment). 
 

Data collection 
At baseline, patients receive a clinical examination and questionnaire. Patients are then given 
a follow-up questionnaire either through email or mail at 3-months, 6-months and 12-months 
follow-up. 
 
 
Description of treatment received during follow-up 
The study participants continue their health care in agreement with their healthcare provider 
regardless of inclusion in the study. This means that patients may receive education and 
advice, exercise therapy, massage, manipulations, mobilizations, pharmacological therapy, or 
additional diagnostic testing and referrals, all of which constitutes “usual” primary care (15). 
Treatment given is at the discretion of the healthcare provider and the patient.  
 
Variables 
Outcome measure:  
Back-related disability, measured using the Norwegian, validated version of the Roland-
Morris Disability Questionnaire (RMDQ) (16, 17). This is a questionnaire with 24 statements 
regarding abilities to perform ADL tasks, with a dichotomous yes/no answer. The answers are 
summed to a total score ranging from 0-24, where 0 indicates no disability and 24 indicates 
“maximum” disability. RMDQ has been found to measure several dimensions of back-related 
disability (18). We plan to use RMDQ as a continuous measure, as recommended by the 
PROGRESS framework (14). This will ensure easier comparability to similar studies, and 
easier inclusion in future meta-analyses. 
 
RMDQ is measured at baseline, 3 months, 6 months and 12 months. 
 
Prognostic factor measurement:  
The prognostic factor of interest is comorbidity measured at baseline with a modified version 
of the Self-administered Comorbidity Questionnaire (SCQ) (19). The original questionnaire 
measures 13 pre-specified comorbidities, and 2 non-specified. The item “back pain” has been 
removed for this study and replaced with an additional non-specified item. Thus, the count of 
comorbidity ranges from 0-15. The diseases listed are: Heart disease, high blood pressure, 
lung disease, diabetes, ulcer or stomach disease, kidney disease, liver disease, anemia or other 
blood disease, cancer, depression, osteoarthritis, rheumatoid arthritis and up to 3 non-
specified comorbidities. The SCQ measures comorbidities on three levels: 1) Do you have the 
problem? 2) Do you receive treatment for it? 3) Does it limit your activities? All levels are 
answered on a dichotomous yes/no level, and you only answer level 2 and 3 if you have 
answered “yes” on level 1. An individual can receive a maximum of 3 points for each medical 
condition: 1 point for the presence of a comorbidity, 1 point if they receive treatment for the 
condition, and 1 point if the condition limits their functioning. The maximum score for the 
full SCQ is thus 45 points.  
 
We plan to use SCQ part 1 comorbidity count (0-15 scale), and the full SCQ (0-45) in 
separate models. Previous studies have found linear relationships between comorbidity and 
back-related disability (7, 11), and thus we plan to treat these variables as linear. Linearity 
with outcome will be assessed, and deviations will be handled appropriately.  
 
 

Covariates: 
Covariates are presented in table 1. They are chosen based on being “established” prognostic 
factors in the literature, having been utilized in similar studies previously (to enhance 
comparability), and for being readily available and easy to measure in clinical practice. 
 
 
Table 1: Covariates, measurement level and/or instrument, rationale for inclusion 
Factor 
Measurement level 
Rationale 
Age 
Continuous, minimum age ≥55 
Standard covariate in previous studies 
(7, 8, 10), and in some studies 
reported to be associated with 
disability levels in older adults (4, 6, 
20) 
Sex 
Dichotomous 
Standard covariate in previous studies 
(7, 8, 10), and in some studies 
reported to be associated with 
disability levels in older adults (6, 20) 
BMI 
Measured continuous, divided into 
categories: <20, 20-25, 25-30, <35 
Covariate in previous study (7), and 
found to be associated with disability 
in older adults (4, 21) 
Back pain duration 
Measured in days, but categorized to 
an ordinal scale of 3 categories: 0-6 
weeks, 6-12 weeks, >12 weeks, 
similar to other BACE studies (22) 
Covariate in previous study (8), and 
found to be associated with disability 
in older adults (4, 6, 11, 20) 
Baseline disability 
Continuous. Roland-Morris disability 
questionnaire, 0-24 scale. 
Standard covariate, given the outcome 
is disability. Only applicable if not 
using mixed models. 
Pain severity last week 
Continuous. Numeric rating scale 0-
10.  
Associated with disability in one study 
in older adults (4). 
Expectation of recovery within the 
next three months 
5-point ordinal Likert scale. From “I 
am fully recovered” to “I am worse 
than ever”. 
Consistently associated with disability 
in older adults (4, 6, 11) 
 
 
Statistical power consideration 
The published protocol for the BACE-N study estimates that we need a total of 450 patients 
included in the study. Allowing a 15% dropout rate at 12 months, we will have approximately 
380 participants available at 12 months. 
 
We used the power estimation tool in Stata to determine level of statistical power. With a 
sample size of 450, and an estimated r2 of 0.30 for the full model of comorbidity adjusted for 
covariates, we have over 90% power to detect an r2-change of 0.017 or higher when adding 
comorbidity to the model.  
 
Handling of missing data 
Missing data will be handled with multiple imputation, using 5 imputations and 10 iterations 
unless the missing exceeds 30% and missing at random cannot be assumed. We will use the 
fully conditioned specification method, and regression estimation. For variables where we are 
unable to use regression estimation due to computational difficulties, predictive mean 
matching will be used (23). 
Missing values for RMDQ will be handled by replacing missing items with the mean of the 
answered items for the individual, if less than 30% of the items are missing.  
A mixed model, which we intend to use for the primary and secondary aim, does not require 
variable level imputation for the outcome measure. However, missing values on item level on 

the RMDQ might still be an issue. To solve this, we will replace missing items for RMDQ 
with the mean of the answered items for the individual, if less than 7 (30%) of the 24 items 
are missing. 
 
 
Statistical analyses 
General statistical considerations 
Analyses described in this statistical analysis plan are considered a priori analyses. Possible 
post-hoc exploratory analyses will be explicitly identified in the article. All analyses will be 
carried out by the first author using Stata version 16, under supervision from the principal 
investigator and the advisory statistician. The statistical tests will be 2-sided, and p-value will 
be reported. If the p-value is less than 0.05, the test is deemed statistically significant. 95% 
confidence intervals will be reported on point estimates.  
 
 
Statistical analyses 
Description of study flow and study sample: 
The flow of participants will be reported with a flow chart. Reasons for exclusion and loss to 
follow-up will be provided where known. Descriptive data of the study sample will be 
reported using mean and standard deviation for normally distributed continuous variables, 
median and interquartile range for variables with skewed distrubution, and with frequency and 
proportions for categorical variables. Normal distribution will be examined visually using 
histogram and QQ-plot, and statistically with the Kolmogorov-Smirnov test. Baseline 
characteristics will be presented in a table. See proposed tables and figures below. 
 
Descriptive statistics will be used to present the mean and standard deviation if RMDQ is 
normally distributed, or median and interquartile range if RMDQ is not normally distributed, 
for each time point: Baseline, 3 months, 6 months, 12 months. This will also be presented 
graphically, similarly to van der Gaag et al (3). This graphical presentation will also be 
performed stratified for number of comorbidities. 
 
A person may be a responder at one time-point and a non-responder at another. Therefore, an 
analysis of responders versus non-responders will be performed for each time point, using 
bivariate analysis for baseline characteristics (chi square test, Individual Samples T-test, or 
Mann Whitney U-test). Results from these analyses will be presented in text, and the table 
available in supplementary material. 
 
 
 
Model choice and model building strategy: 
According to the STRATOS initiative task force, a complete prespecifying of all aspects in 
model building is unrealistic in observational studies (24). Thus, the following will provide a 
framework for analysis and model building in this study, not a detailed recipe. Decisions 
regarding final choice of models will be made by the first author, the advisory statistician and 
the principal investigator.  
 
 

Primary aim, clinical course of back-related disability: 
Mixed models for repeated measures will be used to account for statistical dependencies. 
RMDQ is the dependent variable, and time and first contact health provider will be entered as 
fixed factors. The exact handling of time as a continuous or categorical variable depends on 
the distribution. Previous studies have found that the clinical course of a back episode is not 
linear over time (3, 25, 26). It is therefore reasonable to believe that time will have to be 
treated as a categorical variable, or by introducing a quadratic term, depending on its 
distribution. Choice of covariance matrix is dependent on data structure. Interaction between 
first contact health provider and time will be analysed. 
 
 
Secondary aim, prognostic value of number and severity of comorbidities on the course 
of back-related disability:  
We will fit separate models for count of comorbidity (SCQ part 1, 0-15 scale) and count and 
severity of comorbidity (full SCQ, 0-45). The steps are outlined below: 
 
1. The univariate association between SCQ and RMDQ over time will be assessed with a 
mixed model for repeated measures. An interaction term for SCQ*time will be tested, 
and kept if statistically significant. We will present the crude regression estimates 
from this analysis. 
 
2. A mixed model with all the covariates from Table 1 and RMDQ over time will be 
fitted. From this model, r2 will be presented as a measure of prognostic value. 
 
3. SCQ will be added to the model from step 2. The adjusted regression estimate, and the 
r2-change from step 2 to step 3 will be presented.  
 
Previous studies have found a linear relationship between number and severity of comorbidity 
and disability (7, 27). Linearity between SCQ and RMDQ will be assessed with a scatter-plot. 
In case of non-linearity between SCQ and RMDQ comorbidity count and disability score, the 
following alternatives will be discussed (24, 28):  
- 
Categorization of SCQ 
- 
Transformation (logarithmic or cubic) of SCQ 
- 
Fractional polynomials for SCQ 
- 
Spline functions for SCQ 
 
 
 
 
Sensitivity analyses 
Complete case analyses will be performed to assess possible bias introduced by the multiple 
imputation procedures. 
 
 

Proposed tables and figures 
Figure: Flowchart of study participants 
 
 

Baseline characteristics: 
 
Table: Baseline characteristics 
Variable 
n 
Values 
Sociodemographic variables 
Age, median (IQR) 
 
 
Female, n (%) 
 
 
Married or living with partner, n (%) 
 
 
Education level high, n (%)  
 
 
General health variables 
Health-related quality of life (SF-36), median (range) 
   Physical component score 
   Mental component score 
 
 
Hazardous alcohol consumption (AUDIT-C), n (%) 
 
 
Smoking status, n (%) 
   Current smoker 
   Smoked previously 
   Never 
 
 
Falls efficacy (FES-I), median (range) 
 
 
Back pain history and characteristics of current episode 
First healthcare provider 
   General practitioner 
   Physical therapist 
   Chiropractor 
 
 
History of back pain, n (%) 
 
 
Using pain medication, n (%) 
 
 
Sleep problems weekly due to back pain, n (%) 
 
 
Back pain (NRS 0-10) (figure?), median (range) 
 
 
Disability (RMDQ 0-24) (figure?), median (range) 
 
 
Duration of current episode, n (%) 
   0 – 6 weeks 
   6 weeks – 3 months 
   > 3 months 
 
 
Psychological variables 
Kinesiophobia (FABQ-PA 0-28), median (range) 
 
 
Depression (CES-D 0-60), median (range) 
 
 
Pain catastrophizing (PCS 0-52), median (range) 
 
 
Back beliefs and attitudes (BBQ 9-45), median (range) 
 
 
Expectations of back pain next 3 months (figure?), n (%) 
   Fully recovered 
   Much better 
   No change or worse 
 
 
Psychosocial risk profile (SBT) 
   Low risk 
   Medium risk 
   High risk 
 
 
Clinical variables 
Pain with active movements of the back, n (%) 
 
 
Positive radiculopathy diagnostic rule, n (%) 
 
 
Two or more red flags, n (%) 
 
 
Physical performance (BPS), median (range) 
 
 
Functional mobility (TUG), median (range) 
 
 
SF-36, Short Form health survey 36; AUDIT-C, alcohol use disorders identification test, score ≥3 for women and ≥4 for men ; FES-I, 
Falls Efficacy Scale – International; NRS, numeric rating scale; RMDQ, Roland-Morris Disability Questionnaire; FABQ-PA, Fear-
Avoidance Beliefs Questionnaire – Physical Activity; CES-D, Center for Epidemiological Studies – Depression; PCS, Pain 
Catastrophizing Scale; BBQ, Back Beliefs Questionnaire; SBT, Start Back Tool; BPS, Back Performance Scale; TUG, Timed Up-
and-Go. 
*Comorbidities measured with Self-administered Comorbidity Questionnaire and a question on osteoporosis. 
 
 
 
Appendix table: Baseline characteristics for responders vs non-responders for each time-point.  
Variable 
Responder 
3 mo (n) 
Non-responder 
3 mo 
Responder 6 
mo (n) 
Non-
responder 6 
mo (n) 
Responder 12 
mo (n=) 
Non-
responder 12 
mo (n=) 
Sociodemographic variables 
 
 
 
 
Age, median (IQR) 
 
 
 
 
 
 
Female, n (%) 
 
 
 
 
 
 
Married or living with partner, 
n (%) 
 
 
 
 
 
 
Education level high, n (%)  
 
 
 
 
 
 

General health variables 
 
 
 
 
Health-related quality of life 
(SF-36), median (range) 
   Physical component score 
   Mental component score 
 
 
 
 
 
 
Hazardous alcohol 
consumption (AUDIT-C), n 
(%) 
 
 
 
 
 
 
Smoking status, n (%) 
   Current smoker 
   Smoked previously 
   Never 
 
 
 
 
 
 
Falls efficacy (FES-I), median 
(range) 
 
 
 
 
 
 
Back pain history and characteristics of current episode 
 
 
 
 
First healthcare provider 
   General practitioner 
   Physical therapist 
   Chiropractor 
 
 
 
 
 
 
History of back pain, n (%) 
 
 
 
 
 
 
Using pain medication, n (%) 
 
 
 
 
 
 
Sleep problems weekly due to 
back pain, n (%) 
 
 
 
 
 
 
Back pain (NRS 0-10), median 
(range) 
 
 
 
 
 
 
Disability (RMDQ 0-24), 
median (range) 
 
 
 
 
 
 
Duration of current episode, n 
(%) 
   0 – 6 weeks 
   6 weeks – 3 months 
   > 3 months 
 
 
 
 
 
 
Psychological variables 
 
 
 
 
Kinesiophobia (FABQ-PA 0-
28), median (range) 
 
 
 
 
 
 
Depression (CES-D 0-60), 
median (range) 
 
 
 
 
 
 
Pain catastrophizing (PCS 0-
52), median (range) 
 
 
 
 
 
 
Back beliefs and attitudes 
(BBQ 9-45), median (range) 
 
 
 
 
 
 
Expectations of back pain next 
3 months (figure?), n (%) 
   Fully recovered 
   Much better 
   No change or worse 
 
 
 
 
 
 
Psychosocial risk profile (SBT) 
   Low risk 
   Medium risk 
   High risk 
 
 
 
 
 
 
Clinical variables 
 
 
 
 
Pain with active movements of 
the back, n (%) 
 
 
 
 
 
 
Positive radiculopathy 
diagnostic rule, n (%) 
 
 
 
 
 
 
Two or more red flags, n (%) 
 
 
 
 
 
 
Physical performance (BPS), 
median (range) 
 
 
 
 
 
 
Functional mobility (TUG), 
median (range) 
 
 
 
 
 
 
SF-36, Short Form health survey 36; AUDIT-C, alcohol use disorders identification test, score ≥3 for women and ≥4 for men ; FES-I, 
Falls Efficacy Scale – International; NRS, numeric rating scale; RMDQ, Roland-Morris Disability Questionnaire; FABQ-PA, Fear-
Avoidance Beliefs Questionnaire – Physical Activity; CES-D, Center for Epidemiological Studies – Depression; PCS, Pain 
Catastrophizing Scale; BBQ, Back Beliefs Questionnaire; SBT, Start Back Tool; BPS, Back Performance Scale; TUG, Timed Up-and-
Go. 
*Comorbidities measured with Self-administered Comorbidity Questionnaire and a question on osteoporosis. 
 
 

Prevalence of each comorbidity: 
Table: Prevalence of each comorbidity at baseline 
Comorbidity 
N 
% 
Heart disease 
 
 
High blood pressure 
 
 
Lung disease 
 
 
Diabetes 
 
 
Ulcer or stomach disease 
 
 
Kidney disease 
 
 
Liver disease 
 
 
Anemia or other blood disease 
 
 
Cancer 
 
 
Depression 
 
 
Osteoarthritis 
 
 
Rheumatoid arthritis 
 
 
Osteoporosis* 
 
 
(Other non-prespecified 
comorbidities….) 
 
 
All comorbidities measured by Self-Administered Comorbidity 
Questionnaire 
 
 
 
Example figure presenting clinical course, from van der Gaag et al (3): 
 
 
Proportion of number of comorbidities among patients at baseline, example from Rundell et al 
(29): 
 

 
RMDQ middle value presented at each time point, presented based on number of 
comorbidities (29): 
 
Table: Unadjusted RMDQ score at each timepoint in total cohort and by number of comorbidities 
 
Baseline 
3 months 
6 months 
12 months 
Total cohort 
Xx 
Xx 
Xx 
xx 
No. of comorbidities 
0 
Xx 
Xx 
Xx 
xx 
1 
 
 
 
 
2 
 
 
 
 
3 
 
 
 
 
4 
 
 
 
 
5 
 
 
 
 
6 
 
 
 
 
RMDQ = Roland-Morris Disability Questionnaire 
 
 
 
 
 
 
Association between comorbidities and disability during 1-year follow-up: 
 
Table: Estimates from mixed model(XX) of effect of 
comorbidities on RMDQ score during 1 year of follow-up 
 
 
β 
95% CI 
R2 
Comorbidities~ 
 
 
 
Covariates 
 
 
 
Comorbidities w/covariates* 
 
 
 
RMDQ = Roland-Morris Disability Questionnaire 
~unadjusted effect estimate 
*adjusted for time, age, gender, education level, smoking status, hazardous 
alcohol consumption, back pain duration, probable radicular leg pain, 
(recruitment profession?) 
^adjusted for age, gender, education level, smoking status, hazardous alcohol 
consumption, back pain duration, probable radicular leg pain, (recruitment 
profession?) 
 
 
 
 
 
 

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Dionne CE, Dunn KM, Croft PR. Does back pain prevalence really decrease with increasing 
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van der Gaag WH, Enthoven WTM, Luijsterburg PAJ, van Rijckevorsel-Scheele J, Bierma-
Zeinstra SMA, Bohnen AM, et al. Natural History of Back Pain in Older Adults over Five Years. The 
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Statistical analysis plan (SAP) for:  
External validation of clinical prediction models for non‐recovery in 
older patients seeking primary care for back pain 
 
 
 
 
 
 
 
SAP Authors:  
Ørjan Nesse Vigdal, Kjersti Storheim, Lise Kretz, Tarjei Rysstad, Are Hugo Pripp, Rikke Munk 
Killingmo, Wendelien van der Gaag, Alessandro Chiarotto, Bart Koes, Margreth Grotle (order 
not decided) 
 
Project:  
BACk pain in Elders in Norway (BACE‐N): a prospective cohort study of older people visiting 
primary care with a new episode of back pain 
 
NCT identifier: NCT04261309 
Document date: 28.09.2022 
 
 
 
 
 
 
 

Table of Contents 
SAP Authors: ....................................................................................................................................... 1 
Project: ................................................................................................................................................ 1 
Administrative information ..................................................................................................................... 3 
Scope ................................................................................................................................................... 3 
Working title ....................................................................................................................................... 3 
Version of SAP .................................................................................................................................... 3 
Ethical approval ................................................................................................................................... 3 
Names, affiliations and roles of SAP contributors (order not decided) ............................................... 3 
Study sponsor ...................................................................................................................................... 4 
Signatures ............................................................................................................................................ 4 
Introduction ............................................................................................................................................. 5 
Background and rationale for study .................................................................................................... 5 
Study aim ............................................................................................................................................. 5 
Study design and method ........................................................................................................................ 5 
Study design ........................................................................................................................................ 5 
Study population.................................................................................................................................. 6 
Data collection in validation sample ................................................................................................... 6 
Variables .............................................................................................................................................. 7 
The published clinical prediction models: ........................................................................................... 8 
Sample size calculation ....................................................................................................................... 9 
Statistical analyses ................................................................................................................................... 9 
General statistical considerations ........................................................................................................ 9 
Handling of missing data ..................................................................................................................... 9 
Analyses ............................................................................................................................................ 10 
Sensitivity analyses ........................................................................................................................... 12 
Proposed tables and figures ................................................................................................................... 12 
References ............................................................................................................................................. 14 
 
 
 

Administrative information 
Scope 
This document is a supplement to the BACE-N protocol (ClinicalTrials.gov Identifier: NCT04261309) 
The current SAP was written after data collection was completed, but registered in ClinicalTrials.gov 
before retrieving data from the database for analyses.  
 
Working title 
External validation of clinical prediction models for non-recovery in older patients seeking 
primary care for back pain 
 
Version of SAP 
1.0 
 
Ethical approval 
The BACE-N study was deemed a “quality control project” by the Norwegian Regional Committee for 
Medical Research Ethics, and treatment by the ethical committee was thus not considered necessary as 
per 11.11.2014. Reference: 2014/1634/REK vest 
 
Approval from the Norwegian Social Science Data Service was obtained on 02.03.2015 (reg. no. 
42149).  
 
 
Names, affiliations and roles of SAP contributors (order not decided) 
Name 
Title 
Affiliation 
Role  
Margreth Grotle 
PT, professor, 
PhD 
Oslo Metropolitan University 
P44, 0167 Oslo 
Phone: 90111172  
Email: mgrotle@oslomet.no 
Principal 
Investigator  
Ørjan Nesse Vigdal 
PT, PhD-student Oslo Metropolitan University 
Main author of 
SAP 
Rikke Munk 
Killingmo 
PT, PhD-student Oslo Metropolitan University 
Contributor to 
SAP  
Kjersti Storheim  
PT, professor, 
PhD 
Oslo Metropolitan University 
Contributor to 
SAP 
Are Hugo Pripp  
Statistician, 
PhD 
Oslo Metropolitan University 
Statistical advisor  
Lise Kretz 
DC, PhD-
student 
Oslo Metropolitan University 
Contributor to 
SAP 
Tarjei Rysstad 
PT, PhD-student Oslo Metropolitan University 
Contributor to 
SAP 

Wendelien van der 
Gaag 
MD, PhD-
student 
Erasmus University Medical Center 
Rotterdam 
Contributor to 
SAP 
Bart Koes 
Professor, PhD 
Erasmus University Medical Center 
Rotterdam 
Contributor to 
SAP 
Alessandro 
Chiarotto 
PT, PhD, post-
doc 
Erasmus University Medical Center 
Rotterdam 
Contributor to 
SAP 
 
 
Study sponsor 
The BACE-N-study has received funding from Oslo Metropolitan University, The Norwegian Fund 
for Post-Graduate Training in Physiotherapy (grant number 90749) and “Et liv i bevegelse”, the 
Research Unit of the Norwegian Chiropractic Association. 
 
Signatures 
 
 
 
______________________                ________________________             _______________________ 
Ørjan Nesse Vigdal                      Are Hugo Pripp                             Margreth Grotle 
Main author of SAP                     Statistical advisor                          Principal investigator 
 
 

Introduction 
Background and rationale for study 
Back pain remains the number one cause for years lived with disability globally (1). The 
prevalence of back pain is highest among adults between 40 and 80 years of age (2). This, in 
addition to an ageing population globally, has recently led to increased attention towards back 
pain in older adults (3, 4). Persistent back pain frequently have severe negative consequences 
for the older individual, health care systems and society (5). Thus, identifying those with 
higher risk of persistent levels of back pain or disability is of importance. 
 
Prognostic models are a way of providing individual prognosis, using a standardized 
measurement tool (6). These models combine relevant prognostic factors to try to give an 
accurate prediction on the individual’s prognosis (6). Most previous prognostic models or 
screening tools for back pain are developed for younger adults (7-11). Only one study has  
developed prognostic models for use in older adults with back pain (12). This study, 
performed in the Back Complaints in the Elders (BACE) – Netherlands study, found that a 
combination of biopsychosocial factors was able to accurately predict persistent back pain, 
persistent disability and persistent self-reported non-recovery (12). This model has yet to be 
externally validated, which is a crucial step in prognostic model research (13, 14). The BACE 
consortium, where research groups from different countries use similar methods and 
measurement tools to study back pain in older adults in primary care (3), offers an ideal 
setting for external validation of these prognostic models.  
 
Study aim 
The aim of this study is to externally validate three clinical prediction models for non-
recovery from a new episode of back pain in older adults. If model performance is poor, the 
secondary aim is to recalibrate or update the models. 
 
Here we present the study’s Statistical Analysis Plan (SAP), which details the analysis steps 
for the external validation, to minimize the risk of data selection bias and data-driven 
interpretation of results. 
 
Study design and method 
Study design 
BACE-N is based on the previously published BACE study protocol from the BACE 
international consortium (3). The BACE-N protocol has been published elsewhere 
(ClinicalTrials.gov Identifier: NCT04261309).  
 
The BACE-N study is a prospective observational cohort study with a 2-year follow-up time. 
Patients are followed up after 3, 6, 12 and 24 months. This article will use data from baseline, 
6 and 12 months of follow-up, in line with the original models developed in the BACE 
Netherlands study. The design of the current study was informed by the framework 
PROGnosis RESearch Strategy (PROGRESS), which is a framework for ensuring and 
enhancing the quality of prognostic studies (15). In particular, this is a PROGRESS type 3 
study (6). Reporting will be according to the Transparent Reporting of a multivariable 

prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement for prediction 
studies (16). 
 
Study population 
Development sample, BACE-D: 
Patients aged ≥55 years visiting a general practitioner (GP) in the Netherlands with a new 
episode of back pain were invited to participate. Back pain was defined as pain located in the 
region from the top of the scapula to the sacrum, with or without radiating leg pain. An 
episode of back pain was defined as “new” if the patient has not received health care for the 
same back complaint during the last 6 months.  
 
Exclusion criteria: 
‐ 
difficulty completing the study questionnaires due to language or cognitive difficulties 
‐ 
mobility impairments impeding the clinical examination (wheelchair-bound patients) 
‐ 
had received healthcare for the same back complaint during the last 6 months. 
 
Between March 2009 and September 2011, 675 patients were included. 
 
Validation sample, BACE-N: 
Patients aged ≥55 years visiting a GP, physiotherapist or chiropractor in Norway for a new 
episode of back pain are invited to participate in the study. The inclusion and exclusion 
criteria were identical to the development sample. Between April 2015 and February 2020, 
452 patients were included in the study. 
 
Data collection in validation sample 
Participants were recruited from GP practices, physiotherapy practices and chiropractic 
practices. Healthcare providers were asked to invite consecutive patients, but given obvious 
time constraints in clinical practice they were not asked to collect data on patients that either 
declined to participate or for other reasons were not invited to participate in the study. Eligible 
patients received oral and written information during or after their consultation. Then the 
patients were either contacted by a study coordinator or contacted a study coordinator directly 
for assessment of eligibility. To facilitate the recruitment process, advertisements in media 
were used for inviting eligible patients to directly contact a study coordinator. These patients 
were screened for eligibility by the study coordinators (ØNV, RMK and LK) applying the 
same inclusion and exclusion criteria as mentioned above. 
 
The study participants in BACE-N continue their health care in agreement with their 
healthcare practitioner regardless of inclusion in the study. After the initial clinical 
examination and questionnaire, patients were given a follow-up questionnaire either through 
email or mail at 3-months, 6-months, 12-months and 24-months follow-up. For this study we 
are using baseline data, and from 6 and 12-months follow-up. 
 
 

Variables 
Outcome measures:  
Similarly to the development study (12), non-recovery is defined for three different outcomes: 
 
‐ 
Persistent disability: A score of ≥4/24 on the Roland-Morris Disability Questionnaire 
(RMDQ) at both 6 and 12 months (17, 18). The RMDQ ranges from 0, no disability, 
to 24, maximum disability. 
‐ 
Persistent back pain: A score of ≥3/10 on the Numeric Rating Scale (NRS) at both 6 
and 12 months. The NRS ranges from 0, no pain, to 10, worst maximal pain. 
‐ 
Persistent self-reported non-recovery: A score of ≥3 on a 7-point Global Perceived 
Effect (GPE) scale at both 6 and 12 months. GPE scores were: 
1. Full recovery 
2. Much better 
3. Somewhat better 
4. No change 
5. Somewhat worse 
6. Much worse 
7. Worse than ever 
 
 
Prognostic factors:  
In the development study there were 45 potential predictors. After an extensive literature 
review, consensus meetings, and sample size calculation, 14 candidate predictors were 
selected: 
 
Table 1: Candidate prognostic factors from development study. 
Prognostic factor 
Measurement level 
Measured with 
Parameters in 
model 
Age 
Continuous 
 
1 
Sex 
Binary 
 
1 
Chronic duration of 
current episode of back 
pain 
Binary 
Duration >3 months 
at baseline 
1 
Back pain intensity in 
the past week 
Continuous, 0-10 
NRS 
1 
Back-related disability 
Continuous, 0-24 
RMDQ 
1 
Recent episode of back 
pain 
Binary 
An episode of back 
pain within the past 6 
months, for which 
they did not seek care 
1 
Musculoskeletal 
comorbidity 
Binary 
Modified Dutch 
version of SCQ 
1 
Radiating pain to the 
leg 
Binary 
Presence of radiating 
leg pain in either or 
both leg(s) 
1 
Spinal morning 
stiffness longer than 30 
minutes 
Binary 
Modified KOOS-
question 6a 
1 

Pain during spinal 
rotation 
Binary 
Pain with rotation of 
the upper body 
during clinical 
examination 
1 
Expectations of 
recovery 
Binary.  
Likert scale ranging 
from 1, “fully 
recovered” to 5, 
“worse than ever”. 
Dichotomized into 
“expecting 
improvement” and 
“not expecting 
improvement”. 
1 
Depressive 
symptomology 
Continuous, 0-60 
CES-D 
1 
Kinesiophobia 
Continuous, 0-28 
FABQ-PA 
1 
Pain catastrophizing 
Continuous, 0-52 
PCS 
1 
NRS; Numeric rating scale, RMDQ; Roland-Morris Disability Questionnaire, SCQ; Self-
administered Comorbidity Questionnaire, KOOS; Knee injury and Osteoarthritis Outcome 
Score, CES-D; Center for Epidemiological Studies – Depression, FABQ-PA; Fear-
Avoidance Beliefs Questionnaire – Physical Activity subscale, PCS; Pain Catastrophizing 
Scale. 
 
 
The published clinical prediction models: 
The penalized models from the development samples are as follows: 
 
Persistent back pain = -4.23 + 0.03*Age + 0.73*Chronic duration + 0.23*Back pain intensity 
– 0.10*Back pain intensity (cubic) spline + 0.07*Disability + 0.71*Recent episode + 
0.38*Spinal morning stiffness + 0.42*Pain during spinal rotation – 0.79*Recovery 
expectation 
 
Persistent disability = -5.97 + 0.04*Age + 0.63*Chronic duration + 0.32*Disability – 
0.17*Disability (cubic) spline + 0.40*Recent episode + 0.52*Musculoskeletal comorbidity + 
0.52*Spinal morning stiffness -0.83*Recovery expectation +0.08*Depressive symptomology 
- 0.08*Depressive symptomology (cubic) spline + 0.02*Pain catastrophizing 
 
Self-reported non-recovery = -3.46 + 0.03*age + 0.60*Chronic duration + 0.13*Disability – 
0.07*Disability (cubic) spline + 1.11*Recent episode + 0.36*Pain during spinal rotation - 
0.95*Recovery expectation 
 
 
Changes to candidate prognostic factors and spline terms from development to 
validation 
The development study used a modified version of the Self-Administered Comorbidity 
Questionnaire (SCQ) which was unavailable at the time of designing the external validation 
study. Thus, the validation study uses a Norwegian version of the original SCQ. 
 
Due to using different versions of the SCQ, we are unable to replicate the exact measurement 
of “musculoskeletal comorbidity” in the validation study. Thus, we will exchange this factor 

with “comorbidity” measured with the SCQ as a binary variable with cut-off at ≥1 
comorbidity.  
 
Unfortunately, details on the cubic spline terms in the published original models were 
unavailable in the manuscript, and sufficient details from the development of the spline terms 
were unavailable to the authors of this SAP. This means that we are currently unable to 
replicate the spline terms from the development models in the external validation sample. 
Sufficient details on the spline terms could become available at a later time and may thus be 
incorporated before submission of the article based on these analyses. 
 
Finally, there was a slight difference in wording for the prognostic factor “Spinal morning 
stiffness”. In the Netherlands, patients were asked to indicate if the stiffness lasted longer than 
30 minutes, whereas in Norway, they were asked to indicate if it lasted longer than 60 
minutes. 
 
 
Sample size calculation 
To ensure that the regression analyses are sufficiently powered for model extension, we used 
recent recommendations by Riley et al (19), and their online sample size calculator available 
at https://riskcalc.org/pmsamplesize/. Based on previous studies, we expect a prevalence of 
non-recovery for all outcomes of around 50% (12, 20). It has been suggested that 100-200 
events are sufficient in validation studies (13). We assumed an outcome prevalence of 40%, a 
maximum of 14 candidate predictor parameters and C-statistic values of 0.8, similar to the 
optimism-adjusted C-statistic values from the development study. This yielded a minimum 
sample size of 426 participants.  
 
 
Statistical analyses 
General statistical considerations 
Analyses described in this statistical analysis plan are considered a priori planned analyses. 
Possible post-hoc exploratory analyses will be explicitly identified in the article. All analyses 
will be carried out by the first author (ØNV) using Stata version 16 (StataCorp LLC, Texas, 
USA), under supervision from the principal investigator (MG) and the advisory statistician 
(AHP). The statistical tests will be 2-sided, and p-value will be reported. If the p-value is less 
than 0.05, the test is deemed statistically significant. 95% confidence intervals will be 
reported on effect sizes.  
 
 
Handling of missing data 
Development sample: 
Multiple imputation with 25 imputations and 50 iterations, using predictive mean matching, 
was performed on predictors and outcomes, except for RMDQ, where missingness was 
accounted for by multiplying the total score of the individual by 100, and dividing by the 
number of missing items (12, 21). This 0-100 score was then re-fitted to the original 0-24 
scale (divided the score by 4.167) of the RMDQ.  
 
 
 

Validation sample: 
Missing data on RMDQ at item-level at 6 and 12 months will be handled by replacing the 
missing item with the mean score for that individual, if less than 30% of the RMDQ items is 
missing. This yields identical results to the proposed procedure by Kent et al (21). 
 
Multiple imputation may become necessary if there is substantial missing (>5%), which will 
yield insufficient power in the final models. The risk of this is present because the outcome 
measures used in the models require the participants to have valid answers at both 6 and 12 
months. If multiple imputation of outcome becomes necessary, it will be performed on the 
outcome’s original scale, before dichotomizing the outcomes and combining the time points. 
We will use all predictors and auxiliary variables in the multiple imputation models to reduce 
bias introduced in the models. Observed and imputed values will be compared in tables and 
plots, and convergence will be checked. Number of imputations needed is dependent on if 
convergence is reached, and percentage of participants with missing values (22, 23).   
 
 
Analyses 
Descriptive statistics: 
Descriptive data will be reported for both the development and validation sample, using mean 
and standard deviation for normally distributed continuous variables, median and interquartile 
range for continuous variables with skewed distribution, and with frequency and proportions 
for categorical variables. Normal distribution will be examined visually using histogram and 
QQ-plot, and statistically with the Shapiro-Wilk test.  
 
The proportion of patients with non-recovery will be presented for each outcome for the 
development and validation samples. 
 
Loss-to-follow-up and dropout will be counted and assessed. Reasons for such will be 
presented where available. Patients withdrawing their informed consent during follow-up will 
not be included in the analyses. The baseline characteristics of patients lost to follow-up will 
be compared to those remaining in the study. 
 
External validation: 
We will follow the three steps outlined by Debray et al (24) when assessing external validity: 
 
Step 1, sample relatedness: 
Sample relatedness will be assessed by comparing the patient characteristics in the validation 
sample to the development sample. We will assess each characteristic separately, using 
summary measures like percentage, mean and standard deviation, or median and range. (Table 
3 below.) 
 
Step 2, external validation performance: 
The models tested in the external validation sample looks as follows: 
 
Persistent back pain = -4.23 + 0.03*Age + 0.73*Chronic duration + 0.23*Back pain intensity 
+ 0.07*Disability + 0.71*Recent episode + 0.38*Spinal morning stiffness + 0.42*Pain during 
spinal rotation – 0.79*Recovery expectation 
 

Persistent disability = -5.97 + 0.04*Age + 0.63*Chronic duration + 0.32*Disability + 
0.40*Recent episode + 0.52*Comorbidity + 0.52*Spinal morning stiffness -0.83*Recovery 
expectation +0.08*Depressive symptomology + 0.02*Pain catastrophizing 
 
Persistent self-reported non-recovery = -3.46 + 0.03*Age + 0.60*Chronic duration + 
0.13*Disability + 1.11*Recent episode + 0.36*Pain during spinal rotation - 0.95*Recovery 
expectation 
 
Model performance will be measured with calibration-in-the-large (CITL), calibration slope, 
Nagelkerke’s pseudo-R2, and Area under Receiver Operator Characteristics curve (AUC). 
(Table 4 and Figure 1 below.) CITL and calibration slope is assessed through a calibration 
plot, where observed outcomes are plotted against predicted risks. Perfect calibration is 
indicated by a CITL value of 0 and a calibration slope value of 1 (14). Nagelkerke’s pseudo-
R2 is a measure of overall model performance that ranges between 0 (worst possible 
performance) and 1 (best possible performance). AUC is a measure of the model’s 
discriminative performance. A value of 1 indicates perfect discrimination and a value of 0.5 
indicates discrimination no better than chance. AUC values between 0.7 and 0.8 are 
considered acceptable, between 0.8 and 0.9 excellent, and between 0.9-1.0 outstanding (25).  
 
Step 3, model recalibration and updating: 
If the models perform unsatisfactorily in the validation sample, updating the models may be 
needed. We will assess several alternatives in a step-by-step manner during this stage, 
following the frameworks of Debray et al (24) and Steyerberg (14): 
 
1. Adjustment of model intercept, in case of poor calibration-in-the-large. 
2. Overall adjustment of the calibration slope, in case of poor calibration slope. 
3. In case of poor discrimination in validation models compared to original models, 
model revision may be performed. This involves re-estimation of predictor effects, 
including assessing linearity assumptions of continuous prognostic factors with 
restricted cubic splines or fractional polynomials. The re-estimated predictor effects 
will be corrected for over-optimism by applying a uniform shrinkage factor derived 
from 500 bootstrapped samples.  
4. Model extension, introducing new candidate predictors to the model and assess 
improvement in performance. The candidate predictor to be tested and rationale can be 
found in Table 2 below.  
 
 
Table 2: Candidate prognostic factor for model extension. 
Candidate prognostic factor 
Rationale 
Reduced sleep quality attributable to back 
pain (based on item 5i from PSQI) 
Sleep problems are common in older adults 
(26), and associated with disability in a 
cross-sectional study on back pain in older 
adults (27). Reduced sleep quality often co-
occurs with chronic pain (28-30). 
PSQI: Pittsburgh Sleep Quality Index 
 
 
If model performance after completing a step is acceptable, we will not proceed with further 
steps. 
 

 
Sensitivity analyses 
Complete case analyses will be performed to assess the possible bias introduced from the 
imputation procedure. 
 
 
 
 
Proposed tables and figures 
 
 
Baseline characteristics: 
 
Table 3: Patient characteristics and clinical status at baseline in development sample and validation 
sample. 
 
Development sample, BACE-D 
Validation sample, BACE-N 
 
All participants 
(n=675) 
Missing, n (%) 
All participants 
(n=452) 
Missing, n (%) 
Age, mean (SD) 
 
 
 
 
Female, n (%) 
 
 
 
 
BMI, mean (SD) 
 
 
 
 
Education level high, n (%)  
 
 
 
 
Employment status currently paid work, n (%) 
 
 
 
 
Health-related quality of life (SF-36), mean 
(SD) 
   Physical component score 
   Mental component score 
 
 
 
 
Current smoker, n (%) 
 
 
 
 
Musculoskeletal comorbidity, n (%) 
 
 
 
 
One or more comorbidities, n (%) 
 
 
 
 
One or more previous back pain episodes, n 
(%) 
 
 
 
 
Recent episode of back pain, n (%) 
 
 
 
 
Radiating leg pain, n (%) 
 
 
 
 
Using pain medication, n (%) 
 
 
 
 
Sleep problems weekly due to back pain, n (%) 
 
 
 
 
Average back pain intensity last week (NRS, 
0-10), mean (SD) 
 
 
 
 
Disability (RMDQ, 0-24), mean (SD) 
 
 
 
 
Duration of current episode > 3 months, n (%) 
 
 
 
 
Kinesiophobia (FABQ-PA, 0-24), mean (SD) 
 
 
 
 
Depression (CES-D, 0-60), mean (SD) 
 
 
 
 
Pain catastrophizing (PCS, 0-52), mean (SD) 
 
 
 
 
Expectations of being fully recovered or much 
better from back pain within next 3 months, n 
(%) 
 
 
 
 
Spinal morning stiffness more than 30/60 
minutesa, n (%) 
 
 
 
 
Pain with active rotation of the back, n (%) 
 
 
 
 
SF-36, Short Form health survey 36; AUDIT-C, alcohol use disorders identification test, score ≥3 for women and ≥4 for men; NRS, 
numeric rating scale; RMDQ, Roland-Morris Disability Questionnaire; FABQ-PA, Fear-Avoidance Beliefs Questionnaire – Physical 
Activity; CES-D, Center for Epidemiological Studies – Depression; PCS, Pain Catastrophizing Scale; BBQ, Back Beliefs Questionnaire; 
TUG, Timed Up-and-Go. 
aDuration of morning stiffness was set at 30 minutes in BACE-D, and 60 minutes in BACE-N. 
 
 
 
 

Table 4: Performance measures of external validation (This table will be provided for both the 
original models, and for updated/extended models) 
Persistent back pain (≥3/10 on NRS at both 6 and 12 months) 
Aspect 
Measure 
Value 
Overall performance 
Nagelkerke pseudo-R2 
 
Discrimination 
Area under the Curve 
 
Calibration 
Calibration in-the-large 
 
 
Calibration slope 
 
Persistent back-related disability (≥4/24 on RMDQ at both 6 and 12 months) 
Aspect 
Measure 
Value 
Overall performance 
Nagelkerke pseudo-R2 
 
Discrimination 
Area under the Curve 
 
Calibration 
Calibration in-the-large 
 
 
Calibration slope 
 
Persistent non-recovery (≥3/7 on GPE at both 6 and 12 months) 
Aspect 
Measure 
Value 
Overall performance 
Nagelkerke pseudo-R2 
 
Discrimination 
Area under the Curve 
 
Calibration 
Calibration in-the-large 
 
 
Calibration slope 
 
 
 
 
 
 
 
 
Figure 1: Calibration plot from Traeger et al (8). Calibration plots will be shown for the 
models tested in Step 2, and for possible updated and/or extended models (Step 3). 
 
 

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83. 
 

 
1 
 
Statistical Analysis Plan (SAP) for: 
 
2‐year clinical course of pain intensity and symptom satisfaction for latent 
classes in older adults with back pain 
 
 
 
 
 
 
 
 
 
 
 
 
SAP authors: 
Ann‐Christin Sannes, Lise Kretz, Are Hugo Pripp, Ørjan Nesse Vigedal, Rikke Munk Killingmo, Silje 
Stensrud, Kjersti Storheim, Margreth Grotle, Iben Axén. 
 
 
Project: 
BACk pain in Elders in Norway (BACE‐N): a prospective cohort study of older people visiting primary 
care with a new episode of back pain 
 
NCT identifier: NCT04261309 
Document date: 21.02.2024 
 
 
 
 
 
 
 
 

 
2 
 
Contents 
1. 
Administrative Information ........................................................................................................................... 3 
Introduction to SAP ............................................................................................................................................ 3 
Study ................................................................................................................................................................... 3 
Sponsors ............................................................................................................................................................. 3 
Approvals ............................................................................................................................................................ 3 
Data storage ....................................................................................................................................................... 3 
Version of SAP .................................................................................................................................................... 4 
Names, affiliations, and roles of SAP contributors............................................................................................ 4 
Authors ............................................................................................................................................................... 4 
Abbreviations ..................................................................................................................................................... 5 
Signatures ........................................................................................................................................................... 5 
2. 
Background and objectives ............................................................................................................................ 6 
Background ......................................................................................................................................................... 6 
Objectives ........................................................................................................................................................... 7 
3. 
Study population, design, and method ......................................................................................................... 7 
Study population and recruitment .................................................................................................................... 7 
Study design and setting .................................................................................................................................... 8 
Variables ............................................................................................................................................................. 8 
Outcome measure ........................................................................................................................................... 8 
Prognostic factor ‐ Latent classes .................................................................................................................... 8 
4. 
Statistical principles ........................................................................................................................................ 9 
Statistical analysis .............................................................................................................................................. 9 
Description of study flow ............................................................................................................................... 10 
Handling of missing data .............................................................................................................................. 10 
Model estimation and selection .................................................................................................................... 10 
Sample size .................................................................................................................................................... 10 
5. 
Presentation of the results ........................................................................................................................... 10 
6. 
References .................................................................................................................................................... 15 
 
 
 
 
 

 
3 
 
1. Administrative Information 
 
 
 
 
 
 
Introduction to SAP 
 
This document is a supplement to the BACE‐N protocol (ClinicalTrials.gov identifier: 
NCT04261309) and comprises a statistical analysis plan (SAP) for the article with working title “2‐
year clinical course of pain intensity and symptom satisfaction for latent classes in older adults 
with back pain”.  
 
Study 
BACk pain in Elders in Norway (BACE‐N): a prospective cohort study of older people in 
primary care with a new episode of back pain. 
 
Sponsors 
 
Oslo Metropolitan University, the Norwegian Fund for Post‐Graduate Training in Physiotherapy, 
Norway and Et Liv I Bevegelse (A life in movement – Norwegian Chiropractors’ Research 
Foundation). 
 
Approvals 
The study was classified as a quality assessment study by the Norwegian Regional Committee for 
Medical Research Ethics (reference no. 2014/1634/REK vest) and was approved by the 
Norwegian Social Science Data Service (reference no. 42149) in 2015.  
 
Data storage 
 
Signed informed consent forms are stored in a locked storage. The personal data registry is 
stored in a secure TSD server, accessible only with a project‐specific username, user‐specific 
password and one‐time code. Collected, unidentified data is stored in the TSD secure database. 
Data received through regular mail were manually entered directly into the same database. The 
original paper questionnaires are stored in a locked room – physically removed from the 
informed consent forms.  
 

 
4 
 
Version of SAP 
Version 2.0, 21.02.2024.  
 
Names, affiliations, and roles of SAP contributors 
Scientific board:  
Chris Maher, Bart Koes, Manuela Ferreira, Kjersti Storheim, Liv Inger Strand, Silje Stensrud, Iben 
Axén, Thor Einar Holmgaard, Margreth Grotle.  
Name 
Title 
Affiliation 
Role  
Ann‐Christin Sannes 
DC, PhD‐student 
Oslo Metropolitan University 
Main author of SAP  
Lise Kretz 
DC, PhD 
Oslo Metropolitan University 
Contributor to SAP 
Are Hugo Pripp  
Professor, PhD 
Oslo Metropolitan University 
Statistical advisor  
Ørjan Nesse Vigdal 
PT, PhD‐student 
Oslo Metropolitan University 
Contributor to SAP 
Rikke Munk Killingmo 
PT, PhD 
Oslo Metropolitan University 
Contributor to SAP 
Silje Stensrud 
PT, associate professor, 
PhD 
Oslo Metropolitan University 
Contributor to SAP 
Kjersti Storheim  
PT, professor, PhD 
Oslo Metropolitan University, 
Oslo University Hospital 
Contributor to SAP 
Margreth Grotle 
PT, professor, PhD 
Oslo Metropolitan University 
P44, 0167 Oslo 
Phone: 90111172  
Email: mgrotle@oslomet.no 
Principal 
Investigator  
Iben Axén 
DC, associate professor, 
PhD 
Karolinska Institutet 
 
Contributor to SAP 
 
Authors 
 
Ann‐Christin Sannes, Lise Kretz, Are Hugo Pripp, Ørjan Nesse Vigedal, Rikke Munk Killingmo, Silje 
Stensrud, Kjersti Storheim, Margreth Grotle, Iben Axén  
 
 
 
 
 

 
5 
 
Abbreviations  
 
BACE 
BACk Complaints in the Elderly  
BBQ 
Back Beliefs Questionnaire 
CES‐D 
Center for Epidemiological Studies Depression 
questionnaire 
FABQA‐PA 
FearAvoidance Belief Questionnaire – Physical 
Activity subscale 
GLMM 
General linear mixed model  
LBP 
Low Back Pain 
LCA 
Latent Class Analysis 
NRS 
Numeric Rating Scale 
(PASS) 
Patient Acceptable Symptom State  
OR 
Odds ratio 
PCS 
Pain Catastrophizing Scale  
RMDQ 
Roland‐Morris Disability Questionnaire  
SAP 
Statistical Analysis Plan 
SCQ 
Self‐Administered Comorbidity Questionnaire 
TSD 
Services for Sensitive Data (Tjeneste for 
Sensitive Data) 
 
 
Signatures 
 
 
 
 
______________________ 
 
______________________ 
 
___________________ 
Ann‐Christin Sannes,   
 
Are Hugo Pripp,      
 
                Margreth Grotle 
main author of the SAP 
 
senior statisticial advisor   
principal investigator 
 
 

 
6 
 
2. Background and objectives  
 
 
 
 
 
 
 
Background 
Back pain represents a considerable burden on the society as well as the individual (1–6) and most 
people will experience back pain at some point during their life (7). Among older adults, low back 
pain (LBP) is the most common health problem that results in considerable disability (8,9). Many 
people seek primary health care for their back pain, and those aged 65 years or older is the second 
most common age group to visit physicians for LBP (10).  
 
Back pain is recognized to be an episodic condition, with periods of flare‐ups and (partly) recovery 
(11).  The  condition  is  highly  heterogenic  in  its  presentation  and  course,  and  consequently, 
challenging for clinicians and patients to predict outcome and to decide on the best available 
treatment. Further knowledge on the clinical course of back pain in the older patients may provide 
a better understanding of the variability of recovery. Repeated measurements in cohort studies 
across  different  settings  and  countries  have  identified  common  back  pain  trajectory  patterns, 
reflecting the heterogeneity of back pain course across individuals of different age groups (11–14). 
Previous  evidence  shows  that  for  back  pain  trajectory  studies,  most  studies  have  identified 
between 4 and 5 trajectory patterns for back pain: some differing mainly in terms of severity and 
less in the course patterns, and some differing in the course patterns (i.e., non‐parallel and crossing 
trajectory lines) (for review see (11)). Further,  the  review showed that all studies identified a 
pattern of recovery (except one study that included only LBP patients with at least three months 
duration)  and  a  pattern  of  persistent  severe  LBP  (except  one  study  excluding  patients  with 
unchanging pain due to their analytic approach). Moreover, patterns of more fluctuating pain, i.e., 
alternating LBP intensity and/or by LBP episodes with periods of no pain were observed (11). 
However, all included studies in the review excluded back pain patients >65 years of age.  
 
Recently, some information in older adults have presented, with two large prospective cohort 
studies investigating the course of back pain in older adults. One found that half of the patients 
reported clinically important improvement in back pain intensity, but only 43% showed clinically 
important improvement in back‐related disability after 2 years (15). 17% reported no back pain 
and no back pain‐related disability at 2 years. The other found that 43% of older adults presenting 
to  primary  care  with  back  pain  reported  themselves  as  (almost)  recovered  after  5‐years  (16). 
Moreover, what is considered as clinical improvement may not be as important for the patients. 
Perhaps  adding  an  element  of  symptom  satisfaction  ought  to  be  investigated  to  assess  the 
patient’s perspective on their own condition. One such was to investigate this is by using the 

 
7 
 
Patient Acceptable Symptom State (PASS) (17). Hence, this outcome will also be important to 
explore further and will be included in the present paper.  
This study will build on a latent class analysis (LCA) performed on baseline data (cross‐sectional) 
from this study population (ref/not published yet). The LCA found four classes based on Numeric 
Rating Scale (NRS), Self‐Administered Comorbidity Questionnaire (SCQ), Roland‐Morris Disability 
Questionnaire (RMDQ), Fear‐Avoidance Belief Questionnaire (FABQA), Back Beliefs Questionnaire 
(BBQ),  Center  for  Epidemiological  Studies  Depression  (CES‐D),  and  Pain  Catastrophizing  Scale 
(PCS). These four classes were named “The Positive”, “The Fearful”, “The Negative”, and “The 
Hopeful”. The first class (“The Positive”) had the overall lowest score for reported pain intensity, 
disability, comorbidities, symptoms of depression and pain catastrophizing. The second class (“The 
Fearful”) had the highest score on fear avoidance behaviour and comorbidity, with the second 
highest score on reported pain intensity, disability, catastrophising, and symptoms of depression. 
The third class (“The Negative”) had the highest score on most of the measurements. Of all the 
classes  they  showed  the  highest  pain  intensity,  disability,  symptoms  of  depression,  and  pain 
catastrophising. Moreover, class 3 had the second highest score on both reported comorbidities 
and fear avoidance behaviour. The fourth and final class (“The Hopeful”) had the second lowest 
overall score on all measurements. Unlike class one they had somewhat higher scores on pain 
intensity, disability, fear avoidance behaviour, symptoms of depression, and pain catastrophising.      
Exploring the clinical course of back pain intensity and patient acceptable symptom state based on 
uncovered latent classes may add prognostic value and eventually contribute to better targeting 
the treatment of these patients.  
 
Objectives  
The primary aim is to explore the 2‐year clinical course of back pain and symptom satisfaction 
among older adults seeking primary health care based on classes identified by latent class analysis.  
 
3. Study population, design, and method 
 
 
 
 
Study population and recruitment 
Eligible patients for the BACE‐N study were all people aged 55 years or older seeking primary care 
(general practitioner, physiotherapist, or chiropractor) with a new episode of back pain. A new 
episode was defined as being preceded by 6 months without visiting a primary care provider for 
similar complaints. Patients were excluded from the study if they had a cognitive impairment 

 
8 
 
which precluded them from completing the study questionnaires or if they had difficulties speaking 
and  writing  Norwegian.  Patients  who  had  severe  mobility  impairments  (e.g.,  were  wheelchair 
bound) were excluded if they could not complete the physical examination. While the study was 
ongoing, patients received care as usual.  
Between  April  2015  and  February  2020  patients  were  recruited  from  general  practitioners, 
physiotherapists, and chiropractors working in Norwegian primary care. Several of these practices 
were multidisciplinary clinics.  
After completion of the initial clinical examination and questionnaire, patients were given follow‐
up questionnaires through email or postal mail at 3 months, 6 months, 12 months, and 24 months. 
The participants were informed that they may withdraw from the study at any time without any 
explanations or consequences.  
 
Study design and setting 
This study is using longitudinal data from an observational cohort study with a 2‐year follow‐up in 
a Norwegian primary care setting.  
BACE‐N is part of an international consortium. Details of the BACE‐N are provided in the BACE‐N 
protocol (ClinicalTrials.gov identifier: NCT04261309). 
 
Variables 
Outcome measure 
The first outcome is average pain intensity during the last week (18,19) measured by the Numeric 
Rating Scale (NRS, 0 = no pain ‐ 10 = worst pain imaginable). NRS is a subjective measure for pain 
(20), which is found to be preferable for low back pain cases (21).  
The second outcome is symptom satisfaction (17). PASS consists of a single item: “How satisfied 
would you be if your current symptoms were to persist the rest of your life?”, measured using a 5‐
point likert scale at 3 months, 6 months, and 1‐ and 2‐year follow‐up. The categories are “very 
satisfied”, “somewhat satisfied”, “neither satisfied nor dissatisfied”, “somewhat dissatisfied” and 
“very dissatisfied”. 
Prognostic factor ‐ Latent classes 
A latent class analysis, currently in progress, will be used to identify the number of classes intended 
as prognostic factors in this analysis. The hypothesis is that there may be a difference in the clinical 

 
9 
 
course of the chosen outcomes depending on the probability of belonging to the different latent 
classes mentioned above.  
Potential confounding factors  
In line with the PROGRESS framework and recommendations for type 2 studies (22), we will adjust 
for confounding factors. Confounding factors included in the analysis, based on previous literature, 
related to pain intensity and symptom satisfaction are age (12,15,23), gender (12,24), educational 
level (25,26), employment status (still in paid work), and visited primary health care practitioner 
(27) (see table 1).  
 
4. Statistical principles   
 
 
 
 
 
 
 
Statistical analysis 
All analyses described in this plan are considered a priori in that they have been defined in the 
protocol and/or in this SAP. In case they will be conducted, all post hoc analyses will be identified 
as such in the article. Further, all analyses will be carried out by the main author of this SAP using 
the STATA (StataCorp, College Station, TX, USA) or R.  
To assess for normality of the variables of interest Q‐Q plots and histograms will be used. Normally 
distributed data will be described using mean and standard deviation for continuous variables and 
counts and percentage for categorical data. Skewed data will be presented with medians and 
interquartile ranges. 
Two  separate  analyses  will  be  conducted  for  the  two  chosen  outcomes.  To  investigate  the 
association a general linear mixed model (GLMM) will be conducted to investigate the clinical 
course  of  both  outcomes  within  the  latent  classes  for  all  follow‐up  time  points.  GLMM  is  a 
regression‐based  technique  which  takes  into  account  the  dependency  of  the  repeated 
observations of each individual, by adjusting for the correlations in estimating a variance for the 
intercept,  a  variance  for  the  slope(s)  or  through  different  residual  matrices  (28).  Both  the 
unadjusted and the adjusted model will include the main effect of the classes and the follow‐up 
time points, in addition to the interaction between the two (classes x follow‐up). The adjusted 
model will include the selected covariates mentioned in section 3 (other variables). We will use a 
subject‐specific random intercept. 

 
10 
 
Description of study flow 
The flow of participants throughout all follow‐up points will be visualized using a flow chart (see 
figure 1). 
Participant characteristics 
Baseline characteristics of eligible patients will be presented as shown in table 1. 
Handling of missing data  
Dataset including missing values will be used for the GLMM analyses, with no imputations for 
missing data (29). 
Model estimation and selection 
Model  fit  of  GLMM  will  be  assessed  by  comparing  the  log  likelihood  values  and/or  model  fit 
statistics  as  Akaike  Information  Criterion  (AIC)  of  neighboring  [nested]  models  to  determine 
whether the inclusion of a random slope is necessary and improves the model fit.   
The results will be presented as crude and adjusted beta coefficients (B) with 95% confidence 
intervals (CI) as shown in table 2 and as margin plots with pairwise comparisons.  
Sample size  
This study contains secondary analyses embedded in the BACE‐N study. The BACE‐N study included 
a total of 452 participants which is deemed as an appropriate number of people for such a study 
(30). The number of subjects included for each time point (baseline, 3‐, 6‐, 12‐, and 24 months) 
that answered all questions of interest in relation to the pain intensity analysis was 358, 306, 293, 
281,  and  248,  respectively.  In  regard  to  the  PASS  investigation,  these  numbers  for  the  same 
timepoints were 365, 295, 279, 272, and 230, respectively.   
 
5. Presentation of the results 
 
 
 
 
 
 
 
Table 1. Patient Characteristics at baseline based on classes identified by LCA  (n = ) 
 
Class 1 
Class 2 
Class 3 
Class 4 
Age (mean (SD)) 
 
 
 
 
Gender (n (female)) 
 
 
 
 
Education (n (%)) 
Less than High School 
High School 
 
 
 
 
 
 
 
 

 
11 
 
Higher education <4 years 
Higher education > 4 
years 
Paid work (n (%)) 
Yes 
No 
 
 
 
 
 
 
 
Primary health care 
practitioner (n (%)) 
General practitioner  
Physiotherapist 
Chiropractor 
 
 
 
 
Pain intensity (NRS) 
(mean (SD)) 
 
 
 
 
Symptom satisfaction 
(PASS) (mean (SD)) 
 
 
 
 
SD – standard deviation, n – number, NRS – numeric rating scale, PASS – patient acceptable symptom state 
 
 
Table 2. Estimates of fixed effects parameters from linear mixed models. Showing the association between 
latent classes, pain intensity (NRS) and symptom satisfaction (PASS) over 24 months. 
 
NRS score 
PASS score 
B 
95% CI 
P 
B 
95% CI 
P 
Unadjusted model 
 
 
 
Follow‐up time points 
Baseline (ref) 
3 months 
6 months 
12 months 
24 months 
 
 
 
 
 
 
 
 
 
 
 
 
LCA class 
The Positive (ref) 
The Fearful 
The Negative 
The Hopeful 
 
 
 
 
 
 
 
 
 
 
Interaction Time x LCA 
Baseline x The Positive (ref) 
3 months x The Fearful 
 
 
 
 
 
 
 
 
 
 
 
 
 
   

 
12 
 
3 months x The Negative 
3 months x The Hopeful 
6 months x The Fearful 
6 months x The Negative 
6 months x The Hopeful 
12 months x The Fearful 
12 months x The Negative 
12 months x The Hopeful 
24 months x The Fearful 
24 months x The Negative 
24 months x The Hopeful 
 
 
Full model 
Follow‐up time points 
Baseline (ref) 
3 months 
6 months 
12 months 
24 months 
 
 
 
 
 
 
 
 
 
 
 
 
 
LCA class 
The Positive (ref) 
The Fearful 
The Negative 
The Hopeful 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Interaction Time x LCA 
Baseline x The Positive (ref) 
3 months x The Fearful 
3 months x The Negative 
3 months x The Hopeful 
6 months x The Fearful 
6 months x The Negative 
6 months x The Hopeful 
12 months x The Fearful 
12 months x The Negative 
12 months x The Hopeful 
24 months x The Fearful 
24 months x The Negative 
24 months x The Hopeful 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
13 
 
 
Age 
 
 
 
 
 
 
Gender  
Male (ref) 
Female 
 
 
 
 
 
 
 
 
 
 
 
 
Education  
< High school (ref) 
High school 
< 4 years higher education 
> 4 years higher education 
 
 
 
 
 
 
 
 
 
 
 
 
 
Payed work 
No (ref) 
Yes 
 
 
 
 
 
 
 
 
 
 
 
 
Primary health care 
practitioner 
General practitioner (ref) 
Physiotherapist 
Chiropractor 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
B – regression coefficient, P – p‐value, CI – confidence interval, NRS – Numeric Rating Scale, PASS – Patient Acceptable 
Symptom State, bold numbers indicate statistical significance 
 
 
 
 
 
 

 
14 
 
 
 
Figure 1a. Flow chart of the study NRS 
Figure 1b. Flow chart of the study PASS 
 
Figures will be presented to visualize the pain trajectory patterns for the different classes. 

 
15 
 
6. References 
 
 
 
 
 
 
 
 
 
 
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Brattberg G, Parker MG, Thorslund M. The prevalence of pain among the oldest old in Sweden. Pain. 
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Blyth FM, Cumming R, Mitchell P, Wang JJ. Pain and falls in older people. Eur J Pain. 2007 
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Hartvigsen J, Hancock MJ, Kongsted A, Louw Q, Ferreira ML, Genevay S, et al. What low back pain is 
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Hoy D, Bain C, Williams G, March L, Brooks P, Blyth F, et al. A systematic review of the global 
prevalence of low back pain. Arthritis Rheum. 2012 Jun;64(6):2028–37.  
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Wong AY, Karppinen J, Samartzis D. Low back pain in older adults: risk factors, management options 
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11. 
Kongsted A, Kent P, Axen I, Downie AS, Dunn KM. What have we learned from ten years of trajectory 
research in low back pain? BMC Musculoskelet Disord. 2016 May 21;17:220.  
12. 
Deyo RA, Bryan M, Comstock BA, Turner JA, Heagerty P, Friedly J, et al. Trajectories of symptoms and 
function in older adults with low back disorders. Spine (Phila Pa 1976). 2015 Sep 1;40(17):1352–62.  
13. 
Enthoven WTM, Koes BW, Bierma‐Zeinstra SMA, Bueving HJ, Bohnen AM, Peul WC, et al. Defining 
trajectories in older adults with back pain presenting in general practice. Age Ageing. 2016 Nov;45(6):878–83.  
14. 
Axén I, Leboeuf‐Yde C. Trajectories of low back pain. Best Pract Res Clin Rheumatol. 2013 
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Jarvik JG, Gold LS, Tan K, Friedly JL, Nedeljkovic SS, Comstock BA, et al. Long‐term outcomes of a large, 
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Gaag WH van der, Enthoven WTM, Luijsterburg PAJ, Rijckevorsel‐Scheele J van, Bierma‐Zeinstra SMA, 
Bohnen AM, et al. Natural History of Back Pain in Older Adults over Five Years. J Am Board Fam Med. 2019 Nov 
1;32(6):781–9.  
17. 
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activity state, and patient acceptable symptom state: methodological issues. J Rheumatol. 2005 
Oct;32(10):2025–9.  
18. 
Artus M, Campbell P, Mallen CD, Dunn KM, van der Windt DAW. Generic prognostic factors for 
musculoskeletal pain in primary care: a systematic review. BMJ Open. 2017 Jan 17;7(1):e012901.  
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number and severity of comorbidities in the course of acute non‐specific low back pain in older adults: 
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1 
 
Statistical Analysis Plan (SAP) for: 
 
Development of a clinical prediction model for unfavourable patient 
acceptable symptom state (PASS) in older patients seeking care in Norwegian 
primary health care services for back pain  
 
 
 
 
 
 
 
 
 
 
 
 
 
SAP authors: 
Ann-Christin Sannes, Ørjan Nesse Vigdal, Are Hugo Pripp, Lise Kretz, Rikke Munk Killingmo, Silje 
Stensrud, Iben Axén, Kjersti Storheim, Margreth Grotle 
 
 
Project: 
BACk pain in Elders in Norway (BACE-N): a prospective cohort study of older people visiting primary 
care with a new episode of back pain 
 
NCT identifier: NCT04261309 
Document date: 15.09.2023 
 
 
 
 
 
 

 
2 
 
Contents 
1. 
Administrative Information ............................................................................................................................ 3 
Introduction to SAP ............................................................................................................................................ 3 
Study ................................................................................................................................................................... 3 
Sponsors .............................................................................................................................................................. 3 
Approvals ............................................................................................................................................................ 3 
Version of SAP .................................................................................................................................................... 3 
Names, affiliations, and roles of SAP contributors ............................................................................................ 3 
Authors ............................................................................................................................................................... 4 
Abbreviations ..................................................................................................................................................... 5 
Signatures ........................................................................................................................................................... 5 
2. 
Background and objectives ............................................................................................................................ 6 
Background ......................................................................................................................................................... 6 
Study aim ............................................................................................................................................................ 6 
3. 
Study population, design, and method .......................................................................................................... 7 
Study population and recruitment ..................................................................................................................... 7 
Study design ........................................................................................................................................................ 7 
Variables ............................................................................................................................................................. 8 
Outcome measure ........................................................................................................................................... 8 
Prognostic factor ............................................................................................................................................. 8 
4. 
Statistical analyses .......................................................................................................................................... 9 
General statistical considerations ....................................................................................................................... 9 
Sample size ...................................................................................................................................................... 9 
Handling of missing data ............................................................................................................................... 10 
Descriptive statistics ...................................................................................................................................... 10 
5. 
Presentation of the results ........................................................................................................................... 12 
6. 
References .................................................................................................................................................... 15 
 
 
 
 
 

 
3 
 
1. Administrative Information 
 
 
 
 
 
 
Introduction to SAP 
This document is a supplement to the BACE-N protocol (ClinicalTrials.gov identifier: 
NCT04261309) and comprises a statistical analysis plan (SAP) for the article with working title 
“Development of a clinical prediction model for unfavourable patient acceptable symptom state 
(PASS) in older patients seeking care in Norwegian primary health care services for back pain”.  
 
Study 
BACk pain in Elders in Norway (BACE-N): a prospective cohort study of older people in primary 
care with a new episode of back pain. 
 
Sponsors 
 
Oslo Metropolitan University, the Norwegian Fund for Post-Graduate Training in Physiotherapy, 
Norway, and Et Liv I Bevegelse (A life in movement – Norwegian Chiropractors’ Research 
Foundation). 
 
Approvals 
The study was classified as a quality assessment study by the Norwegian Regional Committee for 
Medical Research Ethics (reference no. 2014/1634/REK vest) and was approved by the 
Norwegian Social Science Data Service (reference no. 42149) in 2015.  
 
Version of SAP 
Version 1.0, 08.07.2023.  
 
Names, affiliations, and roles of SAP contributors  
Scientific board:  
Chris Maher, Bart Koes, Manuela Ferreira, Kjersti Storheim, Liv Inger Strand, Silje Stensrud, Iben 
Axén, Thor Einar Holmgaard, Margreth Grotle.  
Name 
Title 
Affiliation 
Role  
Ann-Christin Sannes 
DC, PhD 
Oslo Metropolitan University 
Main author of SAP  
Ørjan Nesse Vigdal 
PT, PhD-student 
Oslo Metropolitan University 
Contributor to SAP 

 
4 
 
Are Hugo Pripp 
Professor, PhD 
Oslo Metropolitan University 
Statistical advisor  
Lise Kretz 
DC, PhD-student 
Oslo Metropolitan University 
Contributor to SAP 
Rikke Munk Killingmo 
PT, PhD 
Oslo Metropolitan University 
Contributor to SAP 
Silje Stensrud 
PT, associate professor, 
PhD 
Oslo Metropolitan University 
Contributor to SAP 
Iben Axén 
DC, associate professor, 
PhD 
Karolinska Institutet 
 
Contributor to SAP 
Kjersti Storheim  
PT, professor, PhD 
Oslo Metropolitan University, 
Oslo University Hospital 
Contributor to SAP 
Margreth Grotle 
PT, professor, PhD 
Oslo Metropolitan University 
P44, 0167 Oslo 
Phone: 90111172  
Email: mgrotle@oslomet.no 
Principal 
Investigator  
 
Authors 
 
Ann-Christin Sannes, Ørjan Nesse Vigdal, Are Hugo Pripp, Lise Kretz, Rikke Munk Killingmo, Silje 
Stensrud, Iben Axén, Kjersti Storheim, Margreth Grotle  
 
 
 
 

 
5 
 
Abbreviations  
 
AIC 
Akaike information criteria 
AUC 
Area under Receiver Operator Characteristics 
Curve 
BACE 
BACk Complaints in the Elderly  
CES-D 
Center for Epidemiological Studies Depression 
questionnaire 
CITL 
Calibration-in-the-large 
FABQ-PA 
Fear-Avoidance Belief Questionnaire – Physical 
Activity subscale 
KOOS 
Knee injury and Osteoarthritis Outcome Score 
BP 
Back Pain 
NRS 
Numeric Rating Scale 
PASS 
Patient Acceptable Symptom State  
PCS 
Pain Catastrophizing Scale  
RMDQ 
Roland-Morris Disability Questionnaire  
SAP 
Statistical Analysis Plan 
SCQ 
Self-Administered Comorbidity Questionnaire 
TRIPOD 
Transparent Reporting of a multivariable 
prediction model for the Individual Prognosis Or 
Diagnosis 
TSD 
Services for Sensitive Data (Tjeneste for 
Sensitive Data) 
 
 
Signatures 
 
 
 

 
6 
 
______________________ 
 
______________________ 
 
___________________ 
Ann-Christin Sannes,  
 
Are Hugo Pripp,      
 
                Margreth Grotle 
main author of the SAP 
 
senior statistician responsible 
 
principal investigator 
 
2. Background and objectives  
 
 
 
 
 
 
 
Background 
Back pain represents a considerable burden on the society as well as the individual (1–6) which 
effects most people at some point (7). However, back pain has an increased prevalence and 
unfavorable consequences among older adults (8,9). Since persistent, or chronic, pain is difficult 
to successfully treat, including other aspects, such as patient-based evaluation of their own 
symptoms, ought to be included in pain research. One measurement that has gained some traction 
in the recent years is the patient acceptable symptom state (PASS) item (10,11). This may be an 
important tool to monitor the impact symptoms may have on a person’s daily life which could be 
helpful in a treatment management setting. 
 
Another important part of patient management is the use of prognostic models to present a 
possible individual prognosis for the patients. This is done by considering several prognostic factors 
that may be important for accurate prediction (12). The use of prognostic models has yet to be 
extensively investigated in the older population suffering from back pain (13), and has, to the best 
of our knowledge, not been assessed in this patient group in relation to PASS until now.   
 
Study aim 
The primary aim of this study is to create a prediction model for patient acceptable symptom state 
(PASS) among older people seeking primary care for a new episode of back pain over a 24-month 
period. The secondary aim, if deemed appropriate, is to create a clinically useful risk score scale 
based on the most important risk factors from the prediction model.  
 
Here we present the Statistical Analysis Plan (SAP) for the intended study, with the aim of reducing 
the risk of data selection bias and data-driven interpretation of results.  
 

 
7 
 
3. Study population, design, and method 
 
 
 
 
Study population and recruitment 
Eligible patients for the BACE-N study were people aged 55 years or older seeking primary care 
(general practitioner, physiotherapist, or chiropractor) with a new episode of back pain. A new 
episode was defined as being preceded by 6 months without visiting a primary care provider for 
similar complaints. Patients were excluded from the study if they had a cognitive impairment 
which precluded them from completing the study questionnaires or if they had difficulties speaking 
and writing Norwegian. Patients who had severe mobility impairments (e.g., were wheelchair 
bound) were excluded if they could not complete the physical examination. While the study was 
ongoing, patients received care as usual.  
Between April 2015 and February 2020 patients were recruited from general practitioners, 
physiotherapists, and chiropractors working in Norwegian primary care. Several of these practices 
were multidisciplinary clinics.  
After completion an initial clinical examination and questionnaire, patients were given follow-up 
questionnaires through email or postal mail at 3 months, 6 months, 12 months, and 24 months. 
The participants were informed that they may withdraw from the study at any time without any 
explanations or consequences.  
 
Study design 
BACE-N is part of an international consortium, the Back Complaints in the Elders (BACE) (14). 
Details of the BACE-N are provided in the BACE-N protocol (ClinicalTrials.gov identifier: 
NCT04261309). 
The BACE-N study is a prospective observational cohort study with a 2-year follow-up in a 
Norwegian primary care setting. This article will use data from baseline, 12 and 24-months. The 
design of the current study is informed by the framework PROGnosis RESearch Strategy 
(PROGRESS) type 3 (12). Reporting will be according to the Transparent Reporting of a 
multivariable prediction model for the Individual Prognosis Or Diagnosis (TRIPOD) statement 
prediction study (15). 
 

 
8 
 
Variables 
Outcome measure 
The outcome is the patient acceptable symptom state (PASS). PASS consists of a single item: “How 
satisfied would you be if your current symptoms were to persist the rest of your life?”, measured 
using a 5-point likert scale at 1- and 2-year follow-up. The PASS scores is 1) very satisfied, 2) 
somewhat satisfied, 3) neither satisfied nor dissatisfied, 4) somewhat dissatisfied, and 5) very 
dissatisfied. A dichotomization will be done between 2) and 3) where those scoring ³3 at both 12 
and 24 months, will be considered unfavorable. 
 
Prognostic factor  
A prediction model created by the Dutch arm of the BACE consortium (13), which was externally 
validated by our project group (16) investigating pain and disability assessed 14 factors as potential 
prognostic factors. The same factors, in addition to PASS, will be utilized in this work. This is due 
to the overlap of some of the same prognostic factors such as pain (13,17,18) and PASS (19), and 
seems clinically reasonable to investigate. 
Table 1. Potential prognostic factors. 
Prognostic factor 
Measurement level 
Measured with 
Parameters in model 
Age 
Continuous 
 
1 
Sex 
Binary 
 
1 
Chronic duration of 
current episode of back 
pain 
Binary 
Duration >3 months at 
baseline 
1 
Back pain intensity past 
week 
Continuous, 0-10 
NRS 
1 
Back-related disability 
Continuous, 0-24 
RMDQ 
1 
Recent episode of back 
pain 
Binary 
An episode of back pain 
within the past 6 
months, for which they 
did not seek care 
1 
Comorbidity 
Binary 
SCQ 
1 
Radiating pain in the 
leg 
Binary 
Presence of radiating 
leg pain in either or 
both leg(s) 
1 
Spinal morning stiffness 
> 60 minutes 
Binary 
Modified KOOS-
question 6a 
1 
Pain during spinal 
rotation 
Binary 
Pain with rotation of the 
upper body during 
clinical examination 
1 

 
9 
 
Expectations of 
recovery 
Binary 
Likert scale ranging 
from 1, “fully 
recovered” to 5, “worse 
than ever”. 
Dichotomised into 
“expecting 
improvement” and “not 
expecting 
improvement”. 
1 
Depressive 
symptomology 
Continuous, 0-60 
CES-D 
1 
Kinesiophobia 
Continuous, 0-28 
FABQ-PA 
1 
Pain catastrophizing 
Continuous, 0-52 
PCS 
1 
Patient acceptable 
symptom state 
Ordinal, 1-5 
PASS 
1 
NRS; Numeric Rating Scale, RMDQ; Roland-Morris Disability Questionnaire, SCQ; Self-administered 
Comorbidity Questionnaire, KOOS; Knee injury and Osteoarthritis Outcome Score, CES-D; Center for 
Epidemiological Studies – Depression, FABQ-PA; Fear-Avoidance Beliefs Questionnaire – Physical 
Activity subscale, PCS; Pain catastrophizing Scale, PASS; Patient Acceptable Symptom State.  
 
4. Statistical analyses   
 
 
 
 
 
 
 
General statistical considerations 
All analyses described in this plan are considered a priori in that they have been defined in the 
protocol and/or in this SAP. In case they will be conducted, all post hoc analyses will be identified 
as such in the article. Further, all analyses will be carried out by the main author of this SAP using 
the software STATA (StataCorp, College Station, TX, USA), under supervision from the principal 
investigator (MG) and the advisory statistician (AHP). The statistical tests will be 2-sided, and p-
value less than 0.05 will be considered statistically significant. 95% confidence intervals will be 
reported on effect sizes. 
To assess for normality of the variables of interest Q-Q plots and histograms will be used.  
Sample size  
The BACE-N cohort consists of a total of 452 participants. To assess appropriate sample size an 
online sample size calculator available at https://riskcalc.org/pmsamplesize/ was used, as 
recommended by Riley et al (21). Further, previous evidence has proposed that 100-200 events 
will be sufficient (22) for the intended analyses.   

 
10 
 
Handling of missing data  
Missing data for prognostic factors at baseline and PASS at item level at 12-, and 24-months will 
be handled through multiple imputation if necessary (>5% missing), as this may have an impact on 
the final model. The imputation will then be based on the variable’s original scale before 
dichotomization and combining the time points.  
Descriptive statistics 
Descriptive data will be reported using mean and standard deviation for normally distributed 
continuous variables, median and interquartile range for continuous variables with skewed 
distribution, and with frequency and proportions for categorical variables (Table 2).  
Backward selection stepwise regression analyses 
To build the most optimal regression model a stepwise selection approach will be used based on 
the previously validated model. This will be done in three steps. Step 1: fit a regression model using 
all variables considered prognostic factors and calculate the Akaike information criterion (AIC). 
Step 2: remove the factor that has the least significant p-value or causes the lowest drop in R2. 
Step 2 will be repeated until removing any prognostic factors no longer affects or increases the 
AIC. The stopping rule chosen for the purpose of the present paper will be p-value <0.154. A 
graphical comparison will be conducted to assess for linearity for continuous variables. To assess 
the effect of possible non-linearity of continuous variables, a fractional polynomial regression will 
be conducted for comparison.  
Further, a sensitivity analysis will be conducted based on other variable selection methods as 
stepwise regression using other criteria or penalized regression.  
Results from backward selection stepwise regression analyses will be presented as shown in table 
4 for the number of prognostic factors found to be of importance.  
Model performance 
Model performance will be measured by calibration-in-the-large (CITL), calibration slope, 
Nagelkerke`s pseudo-R2, and Area under Receiver Operator Characteristics Curve (AUC), as shown 
in table 3. CITL and calibration slope will be assessed. CITL value equal to 0 and a calibration slope 
of 1 indicates perfect calibration (23). Nagelkerke`s pseudo-R2 measures overall model 
performance that ranges between 0 (worst possible performance) and 1 (best possible 
performance). AUC measures discriminative performance. Ranging from 0 to 1, where 1 is perfect 
discrimination and 0.5 indicates discrimination no better than chance. AUC values between 0.9-
1.0 is considered outstanding, 0.8-0.9 is excellent and 0.7-0.8 is considered acceptable (24). 

 
11 
 
 
Internal validation will be conducted by bootstrapping samples to correct for overoptimism (20). 
The estimated slope value will be used as a shrinkage factor that will be multiplied with the pooled 
coefficients and a new intercept will be determined that aligns with the shrunken coefficients. The 
adjusted linear predictors will be reported in the penalized models and each adjusted models` 
performance will be evaluated with an optimism-adjusted R2 and AUC value.  
Risk score scale 
A risk score scale is a scale that is intended to indicate the risk of certain outcomes in a clinical 
setting. The scale is meant to be quick and easy to use for the healthcare practitioner, which can 
aid in the decision and planning of further treatment.  
The possibility of creating a risk score scale from the results of the regression analyses will be 
considered if deemed clinically appropriate.  
 
 

 
12 
 
5. Presentation of the results 
 
 
 
 
 
 
Below are suggested drafts of tables used for presentation of the results. 
Table 2. Patient Characteristics at baseline  
 
All participants (n = 452) 
Missing, n (%) 
Age, mean (SD) 
 
 
Female, n (%) 
 
 
One or more comorbidities, n (%) 
 
 
Recent episode of back pain, n (%) 
 
 
Duration of current episode >3 months, n (%) 
 
 
Radiating leg pain, n (%) 
 
 
Spinal morning stiffness > 60 minutesa, n (%) 
 
 
Pain with active rotation of the back, n (%) 
 
 
Average back pain intensity last week (NRS, 0-
10), mean (SD) 
 
 
Disability (RMDQ, 0-24), mean (SD) 
 
 
Duration of current episode >3 months, n (%) 
 
 
Kinesiophobia (FABQ-PA, 0-24), mean (SD) 
 
 
Depression (CES-D, 0-60), mean (SD) 
 
 
Pain catastrophizing (PCS, 0-52), mean (SD) 
 
 
Expectations of being fully recovered or much 
better from back pain within the next 3 months, 
n (%) 
 
 
Symptom satisfaction (PASS, 1-5), mean (SD) 
 
 
SD – standard deviation, n – number, SF-36 – Short Form health survey 36, NRS – numeric rating scale, RMDQ – 
Roland-Morris Disability Questionnaire, FABQ-PA – Fear-Avoidance Beliefs Questionnaire - Physical Activity, CES-
D – Center for Epidemiological Studies - Depression, PCS – Pain Catastrophising scale, PASS – patient acceptable 
symptom state 
 
 
 
 

 
13 
 
Table 3. Performance measure for current model 
Persistent dissatisfaction (³3/10 PASS at both 12 and 24 months) 
Aspect 
Measure 
Value 
Overall performance 
Nagelkerke pseudo-R2 
 
Discrimination 
Area under the curve 
 
Calibration 
Calibration in-the-large 
 
 
Calibration slope 
 
 
  
Table 4. The results of stepwise backward regression for dependent variable PASS 
R = , R2 = , Adjust. R2 = , F() = p  
 
Beta Coef 
Standard 
error 
Directional 
coefficient b 
Standard 
error with b 
p-value 
Intercept 
 
 
 
 
 
Prognostic 
factor 1 
 
 
 
 
 
Prognostic 
factor 2 
 
 
 
 
 
Prognostic 
factor 3 
 
 
 
 
 
 
 
 

 
14 
 
 
Figure 1. Calibration plot (25). 
 
A calibration plot like figure 1 will be presented to visualize performance measure of current model.  
 
 

 
15 
 
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