Mental Health, Dysfunctional Eating Pattern and Weight Trajectory in Subjects Undergoing Bariatric Surgery
Conditions: Mental Health Issue, Eating Behavior
Sponsor: GOSEEN Obesity group of the Spanish Endocrinology Society

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Mental health, dysfunctional eating pattern and weight trajectory in subjects 
undergoing bariatric surgery. 
Sponsored by: Working Group of the SEEN (GOSEEN). 
Study name: REGAINSEEN. 
ID: HCB/2019/0090 
 
Summary 
Studying the mechanisms of weight regain (WR) may provide much-needed insight into 
sustained obesity management. The aim of this five-year, prospective, multicentre 
study is to evaluate the association among eating pattern (specifically maladaptive 
behaviours), certain psychological variables and weight trajectory in the short and long 
term after bariatric surgery (BS). The study will include 2 groups: 1.- Candidates to 
primary BS undergoing laparoscopic gastric bypass (LGBP) or laparoscopic sleeve 
gastrectomy (LSG) from September 2020 to September 2022. This group will be 
evaluated prior to surgery, at 4 months, 1 year, 3 years and 5 years after BS and 2.-  A 
control group of subjects with obesity not candidates to BS matched with the 
intervention group for age, sex and BMI prior to BS. The primary variable will be: Body 
weight: total weight lost (%), excess weight lost (%), total weight regained (%), excess 
of weight regain (%). Information regarding the psychological and behavioural 
variables will be collected using questionnaires that have been validated in our setting 
and will be completed by the patients themselves online. 
 
 
 
 

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Introduction and update on the subject  
Obesity is one of the main health problems today, with a prevalence of 20-30% in 
many European countries. Obesity is a chronic, progressive disease without cure that 
requires specific treatment for weight loss and continuous follow-up to avoid weight 
regain [1].  
The objective of the treatment of obesity is to achieve significant sustained weight loss 
(WL), without undernourishment, and with an improvement in body composition and 
associated comorbidities. In this context, bariatric surgery (BS) is, at present, the only 
effective treatment to achieve marked and sustained WL in patients with severe 
obesity, typically yielding a long-term sustained weight losses of 25 to 35% of initial 
weight [2].  
Although the mean data support the effectiveness of BS in the treatment of obesity, 
the magnitude of WL and the maintenance of weight reduction after BS notably differ 
among individuals, even among those undergoing the same surgical technique [3]. 
Data from the pivotal Swedish Obese Subjects (SOS) study show that the maximum WL 
after BS ranged between -95.5 and + 2.0 kg, and weight regain (WR) after nadir was 
between 0.0 and 51.4 kg[4]. This heterogeneity in weight trajectory leads some 
patients to present lower WL than expected and/or the recovery of an important 
amount of weight after nadir.  
At present, it is recognised that the magnitude and maintenance of WL are important 
to the health results of BS. However, one the main problems encountered is how to 
define insufficient WL and pathological WR after BS. A systematic review described 
that the most frequent definition used for insufficient WL after primary BS is a WL < 50% 
of the excess weight present prior to surgery with or without a body mass index (BMI) > 
35 kg/m2 at 18 months after surgery [5]. Thus, insufficient WL is not just the absolute 
WL but rather the deviation of WL from the result that is usually achieved after BS.  
Likewise, in relation to the definition of WR, several studies have used different 
definitions based on kilograms, units of BMI, or percentage of excess WL regained 
after nadir or at different time points, including: regain of 10 kg, regain of 25% of 
excess WL; regain of 15% of initial WL; regain of 5 kg/m2; any WR with a BMI >35 
kg/m2 at nadir, at 18 months or 3-5 years post-BS [6]. Patients who show WR present a 
relapse of not only obesity but potentially also the co-morbidities which had remitted 
during the initial WL. In this case many require revisional surgery or a second procedure 
to re-achieve WL, either with RYGB, or another procedure such as duodenal switch at the 
expense of adding a malabsorptive component to the surgical process, which risks extra 
complications and a high rate of nutritional deficiencies (Annex 1). 
 
The variability of WL or WR has been associated with a wide range of factors related to 
the surgical technique used, psychological factors, behavioural, dietetic factors, 
physical activity and comorbidities as well as biological factors which regulate food 
intake, energy storage and expenditure [7]. Variables such as previous weight, age and 
the type of surgery explain between 40-50% of the variance in WL, with the rest 

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remaining from the variance due to other variables. Although the influence of 
behavioural and psychological factors are of note in studies on WL after BS, the 
identification of predictors is not conclusive or has shown a weak relationship.  
Despite recognition of the important influence of psychological factors on the results 
of BS at a weight level, there are no conclusive data to date [8]. The inconsistencies of 
the results seem to be due to different methodological factors such as the definition of 
the alterations, the evaluation methods used – clinical interview, psychometric tests, 
among others – the different time of evaluation, the time reference assessed and the 
willingness of the candidates to respond in the context of the request for BS. Recent 
systematic reviews have suggested a certain predictive capacity of some factors on 
weight results in regard to their influence on eating behaviour after BS (i.e. presurgical 
cognitive functioning, personality, mental health, some psychological variables and 
dysfunctional eating behaviours) [9][10]. Therefore, not all patients report benefits in 
the psychological domain. In some cases, previous pathology may recur or a new 
psychopathology may arise, impairing the quality of life and interfering in achieving the 
goals proposed. It is not known whether there is a characteristic profile in patients 
with insufficient WL or WR after BS.  
Thus, within the context of the increasing use of BS it is essential to determine the 
causes for this variability in weight trajectory following BS and understand what factors 
have an impact on WR after BS in order to minimise and prevent them. As mentioned 
previously, behavioural and psychological factors are especially important and require 
the most attention in order to understand the influence these factors have on WR.  
Objective:  
The objective of this study is to evaluate the association among eating pattern 
(specifically maladaptive behaviours), certain psychological variables and weight 
trajectory in the short and long term after bariatric surgery.   
 
Design 
Five-year, prospective, multicentre study.   
The study will include 2 groups:  
a) Candidates to primary BS undergoing laparoscopic gastric bypass (LGBP) or 
laparoscopic sleeve gastrectomy (LSG) from September 2020 to September 
2022. This group will be evaluated prior to surgery (a period vulnerable to 
biases by the request for surgery that affect the answers to the questionnaires 
for fear of being contraindicated for surgery). The subjects of this group will be 
followed at 4 months, 1 year, 3 years and 5 years after BS.  
b) A control group of subjects with obesity not candidates to BS matched with the 
intervention group for age, sex and BMI prior to BS. 
*The control group will be recruited by each investigator (10 subjects) and will be 
evaluated once. According to our casuistic, in order to be comparable with the 

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intervention group 80% should be women of 45 years of age with a BMI > 40 kg/m2. 
This group is needed to adjust for variables such as motivation, patient expectations 
related to surgery and the effect of surgery.  
Inclusion criteria: 
• Candidate to primary BS from 09/2010 to 09/2021. 
• Subjects with obesity (>40 kg/m2) not candidates to BS. 
• Internet users. 
• Primary BS: LGBP and LSG. 
• Accept to participate in the study and provide signed informed consent.  
Exclusion criteria: 
• Second time and/or revisional BS. 
• Pregnancy. 
• Presence of intellectual impairment impeding the administration of the 
psychometric tools.  
• Type 1 diabetes. 
• Dyslexia.   
Weight variables: definitions. 
a) The different weight variables will be calculated as follows:  
• Weight lost (kg): Pre-BS weight – weight at nadir. 
• Current weight lost: Pre-BS weight - current weight. 
• Ideal weight: weight corresponding to a BMI = 25 kg/m2. 
• Excess weight lost (%): (Pre-BS weight - current weight)/excess weight [(pre-BS 
weight – ideal weight) x 100].  
• Total weight lost (%): (pre-Bs weight – current weight/pre-BS weight) x 100. 
 
b) To study WR the following variables will be calculated: 
• Total WR (%): (100/(pre-Bs weight – weight at nadir)) * weight regained (kg). 
• Excess WR (EWR %): [(current weight – weight at nadir)/excess weight] x 100 
• Weight regained (Kg): current weight – weight at nadir. 
 
Excess weight lost  > 50%, total weight lost > 25%  and Excess weight regain > 15% will 
be considered as significant. 
 
 
 

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Psychological evaluation:  
Information regarding the psychological and behavioural variables will be 
collected using questionnaires that have been validated in our setting and will 
be completed by the patients themselves online at: 
https://www.surveygizmo.eu/s3/90152075/Patient-Survey. 
The questionnaires include a total of 201 questions, and the survey is 
designed to be completed within 30 minutes. On average, 40 questions of this 
type can be answered in 10 minutes.  
 
To assess hedonic response to the foods, the following questionnaires will be 
used: 
• The subscale of external signal-triggered eating of the Dutch Eating Behaviour 
Questionnaire (DEBQ) that includes 10 questions.  
• A food addiction questionnaire (Yale Food Addiction Scale II) with 25 questions.  
• The Bulimia Investigatory Test Edinburgh (BITE) made up of 2 subscales of 
symptoms and severity including 33 questions.  
• There will also be questions related to snacking/nibbling.  
The following will be used to assess mood state, stress and negative emotions:  
• Dutch Eating Behaviour Questionnaire (DEBQ) which evaluates eating styles 
and takes into account 3 subscales: external, restrained (10), and emotional 
eating (13) including a total of 23 questions. 
• Positive and Negative Affect Schedule (PANAS) to assess the positive and 
negative effects of emotional experience including 20 questions.  
• Barrat impulsivity Scale (BIS-11) which evaluates impulsive tendencies in 3 
dimensions: cognitive, motor and lack of planning and includes 30 questions. 
• The consumption of toxic substances will be determined using the following 
questionnaires: Alcohol Use Disorders Identification Test (AUDIT-4) including 4 
questions in which data on the frequency and quantity of alcohol consumed 
will be obtained, and the Fagerström Nicotine Dependence Scale including 6 
questions.  
• The Eysenck Personality Questionnaire - Revised (EPQ-R) with only the 
dimension of neuroticism including 23 questions.  
These variables will be considered as confounding psychological variables and will, 
therefore, be controlled by statistical tests.  
• Depression: evaluated with the Hospital Anxiety and Depression Scale (HADS) 
including 14 questions.  
• Perceived economic status, assessed using a visual analogue scale of 10 cms 
with markings at 0, 5, 10 cm without numbers. 

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• The International Physical Activity Questionnaire (IPAQ) including 7 questions 
to assess physical activity.   
• Social support evaluated by the evaluation protocol using the English 
Longitudinal Study of Ageing study. APGAR (including 5 questions). 
• Quality of life evaluated using the Impact of Weight on the Quality of Life - Lite 
(IWQOL-lite) questionnaire including 31 questions.  
Table 1: Questionnaires on hedonic response to food, addictive behaviour and mood 
state.  
Hedonic response to food 
Mood state, stress, negative emotions 
DEBQ-external (10) 
DEBQ-restraint and emotional (23) 
Yale Food Addiction Scale II (25) 
Positive and Negative Affect Schedule 
(PANAS) (20) 
Binge eating scale (BITE) (33) 
Barrat Impulsivity Scale (30) 
Confounding variables 
Alcohol Use Disorders Identification Test 
(AUDIT-4). 
Fagerström 
Nicotine 
Dependence Scale (6) 
International 
Physical 
Activity 
Questionnaire (IPAQ) (7). 
EPQR-neuroticism (23) 
APGAR (5) 
Hospital Anxiety and Depression Scale 
(14) 
Quality of life evaluated with the Impact 
on Weight on the Quality of Life – lite 
questionnaire (IWQOL-lite) (31) 
 
 
 
Evaluation of food intake: The patients will also complete a registry of foods and 
drinks consumed over 3 days including a weekend day. It should include the type of 
food, cooking method, and portion size or weight. Afterwards, the total intake and by 
macronutrients will be analysed with the Diet Source software (Nestle Health Science. 
V4.0).  
Primary variable:  
Body weight: total weight lost (%), excess weight lost (%), total weight regained (%), 
excess of weight regain (%). 
Data collection: 
I.Additional information on demographic and anthropometric characteristics 
and on comorbidities both prior to BS and their evolution over time will be 
collected in an Excel datasheet designed with this objective and will include 
the following:  
 
Centre: A number identifying each centre has been assigned starting at 1, with the first 
9 numbers having a 0 before them.  

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Code: To register the data of the patients a 5 digit code will identify each patient, with 
the first 2 numbers corresponding to the number assigned to the centre and the 
following 3 numbers will correspond to the number assigned to the patient upon entry 
in the study. For example, the first patient recruited in Hospital Clinic (corresponding 
to 04) will be assigned the code 001 so that the final code of this patient will be 04001. 
The code will be kept in each centre and confidentiality will be guaranteed by the 
investigator.  
The Excel database will not include the number of the clinical history of the patient or 
any data which could identify the patient. 
 Study group: 0: control; 1: case  
The investigator in each centre should register whether the number of the patient 
corresponds to a case or a control. 
Age: in years 
Sex: 0: man; 1 woman 
Ethnicity: 0: Caucasian, 1: Gypsy; 2: Latin American; 3: other. 
Marital status: 1. Married or with partner; 2. Single; 3. Separated/divorced; 4. Widow. 
Lives with (number of people): 1. Alone; 2. Partner/husband/wife; 3. Children, 
number …; 4. Parents; 5. Elderly parents/in-laws; 6. other 
Occupation: 1. Homemaker; 2. Unskilled worker 3. Skilled worker. 4. Administrative, 5. 
Contract worker; 6. Retailer; 7. Business person; 8. Other. 
Current occupational status: 1. Active/remunerated work; 2. Unemployed; 3. Retired; 
4.Temporary work leave; 5. Permanent work leave; 6. Disability. 
Level of education: The highest level of education achieved will be registered. 1. No 
education; 2.- primary; 3. secondary; 4. technical training or similar; 5. university; 6. 
other. 
Comorbidities registered prior to surgery: 
➢ 
Diabetes:  
0: normal;  
1: altered fasting glucose /intolerance to carbohydrates or previous gestational 
diabetes;  
2: Type 2 diabetes;  
3: Type 2 diabetes with HbA1c > 8% 4:  insulin treatment.  

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If a patient classifies as more than one category, this should be separated with a 
comma. For example, the patient has type 2 diabetes treated with  insulin + HbA1c > 8, 
this would be 2,3,4. 
➢ 
Sleep apnoea  
0: normal or Epworth score < 10;  
1: Epworth > 10, apnoea-hypopnoea index 5-20;  
2: Use of continuous positive airway pressure therapy (CPAP);  
3: Cor pulmonale.   
➢ 
Arterial hypertension (AHT):  
0 Normotensive (blood pressure (BP) < 140/90 mmHg);  
1: treatment with 1 hypotensive drug;  
2: treatment with more than 2 drugs;  
3: decompensated AHT > 140/90 mmHg. 
➢ 
Dyslipidaemia:  
0: no;  
1: treatment with statins;  
2: treatment with fibrates;  
3: treatment with statins + fibrates.  
➢ 
Hight-density lipoprotein (HDL) level < 40 mg/dl: 0: no, 1: yes. 
➢ 
Previous cardiovascular events: 0:no; 1: yes . 
Psychiatric disorder: 0: no; 1: yes. Add diagnosis. 
Variables of weight: Weight in Kg and height in metres at baseline in the two groups; 
weight at 4, 12, 36 and 60 months in the intervention group and in the control group. 
These data will be used to calculate the absolute WL, % of WL, EWL, BMI lost, the total 
weight regained (TWR) and the EWR . 
Surgery: 0: GBP, 1: LSG 
Date of surgery: This will be registered as a date and is in order to adapt the dates of 
the follow-up visits to the different times.  
Follow-up: at 4-6 months, and 1, 3 and  5 years  
Diabetes yes/no; diabetes treatment ( 0: no; 1: metformin, 2: DPPIV inhibition, 3: 
iSGLT2, 4: Sulfonylureas , 5: GLP-1 analogues, 6: insulin). Likewise, combined treatment 

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should be separated with a comma. For example, a patient controlled with metformin 
+ iSGLT2 would be 1,3. 
AHT yes/no; AHT treatment (0: no; 1: treatment with 1 drug; 2: treatment with  > 2 
drugs);  
Dyslipidaemia: 0: No, 1: Yes; treatment: 0: no, 1. Treatment with statins, 2: treatment 
with fibrates, 3: treatment with statins + ezetimibe. 
Sleep apnoea hypopnoea syndrome (SAHS) yes/no; Use of CPAP yes/no;  
Revision surgery: 0: no, 1: yes.  
Date of revision surgery: registered in date format. 
Analytical parameters: fasting glucose, insulin, HbA1c, creatinine,  total cholesterol, 
HDL. 
Bioimpedance: fat mass (FM) %; FM kg; fat free mass:  (whatever is available). 
Sample size  
The study will include 445 patients undergoing BS. It is estimated that the percentage 
of losses to follow-up at 5 years will not be greater than 10% and thus, at least 400 
patients must achieve complete follow-up. This sample size will have a statistical 
power of 80% for detecting an effect size, that is, differences divided by the standard 
deviation of at least  0.281[11]–[13] in magnitude; this effect size is considered in the 
literature as being of small magnitude [14], [15], and consequently the statistical 
power will be greater for greater magnitudes. The study will also be discriminatory for 
binary variables presenting an association with an odds ratio (OR) of at least  1.78[11], 
[16], [17]. Finally, correlations  0.6[18]–[21] will be detected considering that values <  
0.5 are not relevant. In the three cases cited the calculations of sample size were made 
using a bilateral level of significance of 5% and a statistical power of 80%. This sample 
size will allow adjusting possible confounding factors and generate a multivariate 
model with a minimum number of covariables (at least 4-7, depending on the final 
number of events and correlations) to obtain adjusted estimators [22]–[24]. 
Additionally, 100 controls not undergoing BS will be included and matched for age, 
gender and BMI with the objective of standardising the values in the scales. The ratio 
will be 1:4 for controls and patients undergoing BS, respectively.  
Statistical analysis  
The design and statistical analysis of the present study meet the recommendations of 
consensus documents in the literature [25]–[28], specially the TRIPOD statement [29]. 
A statistical analysis plan (SAP) will be elaborated [30] prior to finalisation of data 
collection and will provide an in depth description of the statistical methods to be used, 
the tables and figures that will be included in the statistical report as well as the 

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strategy to follow in the case of missing values [31], [32], and multiplicity [33] adapted 
to regulatory [34] and scientific recommendations [26].  
Descriptive statistics will be used according to standard calculation methods [35]. To 
elaborate a predictive model to a determined time, logistic regression models will be 
used [36]–[39]. For time variables up to a determined event, survival analysis 
techniques will be used. The function of survival as well as the median of time to the 
event (confidence interval of 95% [CI 95%]) will be estimated using the Kaplan-Meier 
method. Estimations of risk will be made with the classical [40] and extended [41] Cox 
proportional hazards model. Covariables will be tested for the assumption of 
proportional hazards. Initially all the possible covariables will be analysed in a 
univariate analysis, and factors with p < 0.1 will be included in a multivariate model. All 
the variables not selected for inclusion (p >= 0.1) will, in turn, be checked in the final 
model to determine whether their inclusion would improve adjustment of the model 
by a p value < 0.1 or a value less than Akaike. Continuous variables will be tested as 
continuous linear variables, categorical variables (that is, quartile and predefined for 
clinical reasons) or continuous non-linear variables based on previously published 
literature, clinical value and goodness-of-fit models.  
The following is a summary of the general approach of inferential statistical analysis. 
The Fisher exact test will be used to compare categorical variables. For continuous 
variables between the two groups, the Student’s t test and ANOVA for more than two 
will be used. In the case of not fulfilling the assumptions of applicability or of ordinal 
variables, non-parametric methods (Mann-Whitney U test or Kruskall-Wallis) or an 
ANOVA will be used with previous transformation of the dependent variable to ranges 
to evaluate more than one factor concomitantly (rank-ANOVA). 
Variables repeated over time will be analysed as follows:  (i) continuous variables, such 
as the evolution of the variables of weight, with  longitudinal mixed model for 
repeated measurements (MMRM)[42], [43]; (ii) Binary variables will be analysed using 
Generalised Linear Mixed Models (GLMM). Marginal models will be used (Generalised 
Estimating Equation (GEE) for the analysis of sensitivity [44][45].  
The results will be analysed with the SAS version 9.4 statistical package (or a higher 
version) [46] (SAS Institute Inc., Cary, NC) or with equivalent validated software. The 
level of significance will be bilateral at 5%. 
Ethical Committee  
The protocol should be approved by the Ethical Committees of the participating 
centres. An informed consent sheet will be included together with the project for 
presentation to the Ethical Committee of each centre. Informed consent will include 
special emphasis on the confidentiality of the personal data and that these data will 
not be able to be identified. Following receipt of approval, the investigators must send 
a copy of the approval and the number assigned for incorporation in the project file. 
The original should remain in the centre and its safety is the responsibility of the 
centre investigator.  

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The results of the centres that do not obtain approval from the Ethical Committee will 
not be used in the analyses.  
Treatment of missing data  
Missing data will be registered in box x to ensure that the data cannot be recovered. If 
the box remains empty, there will continue to be doubt about whether the data can be 
recovered. 
  
 
 

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References 
[1] 
P. Bjorntorp et al., “Obesity : Preventing and Managing the Global Epidemic,” 
WHO Tech. Rep. Ser., p. 253, 2000. 
[2] 
A. P. Courcoulas et al., “Long-term outcomes of bariatric surgery: A National 
Institutes of Health symposium,” JAMA Surg., vol. 149, no. 12, pp. 1323–1329, 
2017. 
[3] 
A. P. Courcoulas et al., “Seven-year weight trajectories and health outcomes in 
the Longitudinal Assessment of Bariatric Surgery (LABS) study,” JAMA Surg., vol. 
153, no. 5, pp. 427–434, 2018. 
[4] 
L. Sjöström, “Review of the key results from the Swedish Obese Subjects (SOS) 
trial - a prospective controlled intervention study of bariatric surgery,” J. Intern. 
Med., vol. 273, no. 3, pp. 219–234, 2013. 
[5] 
J. P. Mann, A. D. Jakes, J. D. Hayden, and J. H. Barth, “Systematic Review of 
Definitions of Failure in Revisional Bariatric Surgery,” 2014. 
[6] 
M. Lauti, D. Lemanu, I. S. L. Zeng, B. Su’a, A. G. Hill, and A. D. MacCormick, 
“Definition determines weight regain outcomes after sleeve gastrectomy,” Surg. 
Obes. Relat. Dis., vol. 13, no. 7, pp. 1123–1129, 2017. 
[7] 
C. J. De Gara and S. Karmali, “The anatomy of a weight recidivism and revision 
bariatric surgical clinic,” Gastroenterol. Res. Pract., vol. 2014, 2014. 
[8] 
A. J. Dawes et al., “Mental health conditions among patients seeking and 
undergoing bariatric surgery a meta-analysis,” JAMA - J. Am. Med. Assoc., vol. 
315, no. 2, pp. 150–163, 2016. 
[9] 
C. A. Linte, J. J. Camp, M. E. Rettmann, D. R. H. Iii, and A. Richard, “HHS Public 
Access,” vol. 25, no. 2, pp. 1–13, 2015. 
[10] 
C. L. Wimmelmann, F. Dela, and E. L. Mortensen, “Psychological predictors of 
mental health and health-related quality of life after bariatric surgery : A review 
of the recent research,” 2013. 
[11] 
J. D. Elashoff, “nQuery Advisor .... Version 7.0 User’s Guide.” Los Angeles, CA, 
2007. 
[12] 
W. Dixon and F. Massey, Introduction to Statistical Analysis, 4th editio. McGraw-
Hill College, 1983. 
[13] 
R. O’Brien and K. Muller, “Applied Analysis of Variance in Behavioral Science,” 
New York: Marcel Dekker, 1993, pp. 297–344. 
[14] 
J. Cohen, Statistical Power Analysis for Behavioral Sciences, 2nd ed. 1988. 
[15] 
J. Cohen, “A power primer.,” Psychol. Bull., vol. 112, no. 1, pp. 155–9, Jul. 1992. 
[16] D. Machin and M. Campbell, Statistical Tables for Design of Clinical Trials. Oxford, 
1987. 
[17] 
J. L. Fleiss, A. Tytun, and H. K. Ury, “A simple approximation for calculating 

13 
 
REGAINSEEN 
Versión 2, 07/03/2019 
 
sample sizes for comparing independent proportions.,” Biometrics, vol. 36, no. 2, 
pp. 343–6, Jun. 1980. 
[18] NCSS LCC, Ed., PASS 16 Power Analysis and Sample Size Software. Kaysville, Utah, 
2018. 
[19] 
F. Graybill, An Introduction to Linear Statistical Models. New York, 1961. 
[20] 
W. Guenther, “’Desk Calculation of Probabilities for the Distribution of the 
Sample Correlation Coefficient,” Am. Stat., vol. 31, no. 1, pp. 45–48, 1977. 
[21] 
J. Zar, Biostatistical Analysis, 2nd ed. Englewood Cliffs, New Jersey, 1984. 
[22] 
D. Freeman, Applied categorical data analysis. New York, 1987. 
[23] 
P. Peduzzi, J. Concato, E. Kemper, T. R. Holford, and A. R. Feinstein, “A 
simulation study of the number of events per variable in logistic regression 
analysis.,” J. Clin. Epidemiol., vol. 49, no. 12, pp. 1373–9, Dec. 1996. 
[24] 
E. Vittinghoff and C. E. McCulloch, “Relaxing the rule of ten events per variable 
in logistic and Cox regression.,” Am. J. Epidemiol., vol. 165, no. 6, pp. 710–8, Mar. 
2007. 
[25] 
I. Simera, D. Moher, A. Hirst, J. Hoey, K. F. Schulz, and D. G. Altman, 
“Transparent and accurate reporting increases reliability, utility, and impact of 
your research: reporting guidelines and the EQUATOR Network.,” BMC Med., vol. 
8, p. 24, Apr. 2010. 
[26] 
“EQUATOR-network (Enhancing the Quality and Transparency of Health 
Research).” . 
[27] 
J. F. Cohen et al., “STARD 2015 guidelines for reporting diagnostic accuracy 
studies: explanation and elaboration.,” BMJ Open, vol. 6, no. 11, p. e012799, 
2016. 
[28] 
J. Kottner et al., “Guidelines for Reporting Reliability and Agreement Studies 
(GRRAS) were proposed.,” J. Clin. Epidemiol., vol. 64, no. 1, pp. 96–106, Jan. 
2011. 
[29] 
G. S. Collins, J. B. Reitsma, D. G. Altman, and K. G. M. Moons, “Transparent 
reporting of a multivariable prediction model for Individual Prognosis or 
Diagnosis (TRIPOD): the TRIPOD statement.,” J. Clin. Epidemiol., vol. 68, no. 2, pp. 
134–43, Feb. 2015. 
[30] 
C. Gamble et al., “Guidelines for the Content of Statistical Analysis Plans in 
Clinical Trials,” JAMA, vol. 318, no. 23, p. 2337, Dec. 2017. 
[31] 
European Medicines Agency, “Missing data in confirmatory clinical trials 
(CPMP/EWP/1776/99 Rev. 1),” 2010. . 
[32] 
G. Molenberghs and M. Kenward, Missing Data in Clinical Studies. West Sussex, 
2007. 
[33] 
European Medicines Agency, “Points to Consider on Application with 1. Meta-

14 
 
REGAINSEEN 
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analyses; 2. One pivotal study (CPMP/EWP/908/99),” 2002. . 
[34] 
European Medicines Agency, “Human regulatory, Research and development: 
Scientific guidelines. Clinical Efficacy and Safety. Biostatistics,” 2002. . 
[35] 
P. Armitage, G. Berry, and J. Matthews, Statistical methods in medical research. 
Oxford, 2002. 
[36] 
D. W. Hosmer, T. Hosmer, S. Le Cessie, and S. Lemeshow, “A comparison of 
goodness-of-fit tests for the logistic regression model.,” Stat. Med., vol. 16, no. 9, 
pp. 965–80, May 1997. 
[37] 
D. Hosmer and S. Lemeshow, Applied logistic regression, 2nd ed. New York, 
2000. 
[38] 
B. Ripley, Pattern Recognition and Neural Networks. Cambridge University Press, 
1996. 
[39] 
R. Prentice and R. Pike, “Logistic Disease Incidence Models and Case-Control,” 
Biometrika, vol. 66, pp. 403–411, 1979. 
[40] 
D. R. Cox, “Regression Models and Life-Tables,” J. R. Stat. Soc. Ser. B, vol. 34, no. 
2, pp. 187–220, 1972. 
[41] 
T. Therneau and P. Grambsch, Modeling Survival Data: Extending the Cox Model 
(Statistics for Biology and Health), 1st ed. 2000. 
[42] 
G. Verbeke and G. Molenberghs, Linear Mixed Models for longitudinal Data. 
New York, 2000. 
[43] 
H. Brown and R. Prescott, Applied Mixed Models in Medicine. New York: Wiley & 
Sons, 1999. 
[44] 
C. McCulloch and S. Searle, Generalized, Linear, and Mixed Models. New York: 
Wiley & Sons, 1999. 
[45] 
P. McCullagh and J. Nelder, Generalized Linear Models. London: Chapman and 
Hall, 1989. 
[46] 
“SAS version 9.4 software, SAS Institute Inc., Cary, NC.” .