North Carolina Prostate Cancer Comparative Effectiveness & Survivorship Study (NC ProCESS)
Conditions: Cancer of Prostate, Cancer of the Prostate, Prostate Cancer, Prostatic Cancer
Sponsor: UNC Lineberger Comprehensive Cancer Center

 
 
 
North Carolina Prostate Cancer Comparative Effectiveness & 
Survivorship Study (NC ProCESS) 
 
 
National Clinical Trial (NCT) Identification Number: NCT02564120 
Principal Investigator: Ronald C. Chen 
1 Jan 2019 
 
 

Study Protocol 
Brief Summary: 
NC ProCESS is a cohort of patients from diverse backgrounds diagnosed with early prostate 
cancer, who were enrolled from January 2011-June 2013. These patients were recruited 
throughout North Carolina, and also in partnership with institutions across the country. Patients 
enrolled before they start treatment, and are then followed prospectively through treatment 
and then afterwards. This observational study collects information on quality of life, cancer 
control, and health care received inclusive of treatment and management of subsequent effects 
including complications and recurrence. The objective of this study is to examine comparative 
outcomes among different modern prostate cancer treatment options in this cohort of 
patients. 
Detailed Description: 
Localized prostate cancer treatment options is consistently a "highest priority" comparative 
effectiveness research (CER) topic according to the Institute of Medicine, Agency for Healthcare 
Research and Quality (AHRQ), and other summary reports. Patients urgently need information 
on the comparative outcomes of modern treatment options to guide decision-making for this 
disease that causes significant burden based on its high prevalence, mortality and treatment 
effects on quality of life. The status quo as it pertains to prostate cancer is significant 
overtreatment causing potential patient harm, rapid diffusion of new/expensive technologies 
without proven benefit and patients lacking high quality research evidence to balance direct-to-
consumer advertising and guide individualized decision-making. 
NC ProCESS is a population-based cohort designed specifically to address well-described 
knowledge gaps. It was designed in close collaboration with the unique AHRQ consortium 
stakeholder group, which included representatives from patients, clinicians and policymakers. 
Stakeholders helped define study design to emphasize "real-world" patients and select patient-
centered and relevant outcomes, and have been involved throughout assembly of this patient 
cohort. The diverse cohort is well-represented by "hard to reach" patients; enrollment before 
treatment avoids biases with participation and recall. As clinical trials are not feasible to 
address the central questions in prostate cancer CER, this prospective study will yield the 

highest level of evidence to inform patients and other stakeholders. With an assembled cohort, 
this study is necessary to examine comparative outcomes. 
Study Design: 
Study Type: 
Observational 
Actual Enrollment: 
1656 participants 
Observational Model: 
Cohort 
Time Perspective: 
Prospective 
Study Start Date: 
January 2011 
Estimated Primary Completion Date: December 2018 
Estimated Study Completion Date: 
December 2018 
 
Outcome Measures: 
Primary Outcome Measure:  
1. Patient Reported Outcomes [Time Frame: 4 years]  
Directly compare patient-reported prostate-cancer specific and global quality of life 
(QOL), anxiety regarding prostate cancer and decisional regret in a cohort of men with 
localized prostate cancer managed by active surveillance, radical prostatectomy, 
radiation therapy, and brachytherapy. 
2. Disease-Free Survival [Time Frame: 5 years]  
Directly compare the disease-free survival among different treatment options for 
prostate cancer. 
3. Directly compare health care utilization among different treatment options for prostate 
cancer from review of medical records [Time Frame: 4 years]  
For each treatment group (radiation therapy, prostatectomy, active surveillance), we 
will describe the cumulative utilization for each category of health care. These 
categories are physician/specialty visits, hospitalizations, diagnostic tests/procedures, 
medications, and prostate cancer treatments (for the initial cancer and for recurrence). 
Measurements will be attained by review of medical records. 
Eligibility Criteria: 
Ages Eligible for Study: 
 
35 Years to 80 Years  
Sexes Eligible for Study: 
 
Male 

Accepts Healthy Volunteers:  
No 
Sampling Method: 
 
Non-Probability Sample 
Study Population 
Newly-diagnosed, early stage (localized, non-metastatic) prostate cancer patients 
Inclusion Criteria: 
• 
Newly-diagnosed, histologically-proven, localized prostate adenocarcinoma. 
• 
Completion of baseline interview prior to initiating therapy. 
• 
Patient ability to complete study interview: no cognitive impairment, language or 
hearing problems. 
• 
Not diagnosed with prostate cancer through transurethral resection of the prostate 
(TURP). 
• 
Age 35-80. 
• 
English speaking. 
• 
Has telephone. 
Exclusion Criteria: 
• 
Initiation of treatment for prostate cancer prior to completion of baseline interview. 
• 
Cognitive impairment. 
• 
Hearing problems. 
• 
Inability to speak or understand English. 
 
 

Analytical and Statistical Approaches 
Aim 1: Consistent with the prostate cancer QOL literature, we treated each patient-reported 
outcome measure as a continuous variable. We described the mean, standard deviation, and 
range of each measure at each time point, as well as the change from each time point 
compared to the baseline measure. Differences among treatment groups were compared using 
t-tests. A unique aspect of the PCSI instrument is that scores in each domain (Urinary 
Incontinence, Urinary Obstruction/Irritation, Bowel Problems, Sexual Dysfunction) are 
translatable into functional categories: normal, intermediate, and poor.2,37 Thus, we explored 
QOL measures using functional categories to facilitate creation of patient-friendly 
dissemination materials that summarize study findings. Further, to examine differences in 
outcomes across treatments, we conducted linear regression models applying propensity 
scores; a separate model was constructed for each outcome measure. We used generalized 
estimating equations (GEE) to account for correlation of the outcome measured repeatedly 
over time. In addition, we included time as a categorical variable and added a time*treatment 
interaction in the models to account for a non-linear change in outcome over time.  
 
Aim 2: We estimated DFS at 5 years among different treatment groups, using the Kaplan-Meier 
product-limit estimate of the survival curve. We also used the logrank statistic to assess for 
differences in DFS among the different treatment groups. Cox proportional hazards models 
were performed with the application of propensity scores. These Cox models estimated the 
hazard rate for each treatment type and evaluated differences in rates (rate ratios) between 
treatments, adjusting for confounding using propensity scores. We examined the proportional 
hazard assumption in each model and included time-varying covariates or incorporated a 
time*treatment interaction into the model when this assumption was not met.  
 
Aim 3: We described the cumulative utilization for each category of health care 
(physician/specialty visits, hospitalizations, diagnostic tests/procedures, medications, prostate 
cancer treatment) within 4 years by treatment group. Specifically, we examined the proportion 
of patients in each treatment group who received each category of care (we created a 

dichotomous variable indicating whether the patient received the care), and the count of 
services in each care category. We tested the differences in proportions between treatment 
groups using chi-square tests and differences in counts between treatment groups using t-tests. 
In multivariable analyses, we used logistic regression models and propensity score methods to 
estimate differences among groups in the proportions of patients receiving each category of 
care. Linear regression models incorporating propensity score methods were used to test 
differences among groups in the number of services received in each category. When counts of 
health care utilization were small (e.g., hospitalization) Poisson regression models were used.    
 
Bias and Confounding: The use of the NCCCR RCA enabled all study participants to enroll prior 
to the initiation of treatment, thereby avoiding potential biases related to participation based 
on post-treatment outcomes. Furthermore, all baseline QOL data were collected before 
treatment, avoiding biases inherent to the recall of baseline information due to patient 
experiences during and after treatment. The following analytical methods were also employed 
to minimize and account for potential bias and confounding. 
1. Propensity score methods: We conducted propensity score analysis to assess for 
potential bias from measured confounders between treatment groups. Specifically, we 
used logistic regression to estimate the probability of receiving each treatment based 
on baseline characteristics and compute the propensity score from the model predicted 
probability of treatment for each participant. We examined the distribution of 
propensity scores across treatment groups and trimmed observations from the non-
overlapping ends of the distribution as appropriate.57 We then created inverse 
probability of treatment weights (IPTW) and stabilized these weights to reflect the 
sample size of each treatment group. IPTW was used in multivariable analyses to 
maximize the balance of confounding variables between the treatment groups. 
Propensity score matching using a “greedy algorithm”58 and an alternative “optimal 
match”59 algorithm was also considered as an approach to balance covariates across 
treatment groups. 

2. Assessment of sample balance: To ensure balance of sample characteristics, we 
compared differences between treatment groups in the mean (for continuous 
variables) or distribution (for categorical variables) of each covariate by applying the 
generated propensity score weights. In addition, for propensity score-matched 
samples, we assessed the balance of study covariates using standardized differences 
across treatment groups. We added interaction terms to the propensity score model 
for the sample characteristics and risk factors that remained unbalanced after applying 
initial propensity score methods. When imbalances remained, we included the 
unbalanced variables in the multivariable outcome model(s) to ensure proper control of 
these confounding factors.   
3. Instrumental variables (IV): We explored instrumental variable approaches to 
estimating the relationship between treatment and outcome while addressing potential 
unmeasured confounders. This method requires finding an appropriate “instrument” 
that is related to the exposure (treatment) but not directly related to the outcome, and 
meets all underlying IV assumptions.60   
4. Handling of missing data: We followed the PCORI standards for handling and 
minimizing the impact of missing data. First, we carefully examined the pattern of 
missing data by individual items. We determined whether the missing data is 
informative, missing not at random (MNAR), missing completely at random (MCAR), or 
missing at random (MAR) by evaluating patient characteristics (e.g., age, race and 
ethnicity, treatment). We also compared those patients with missing data to patients 
with complete data. We then imputed missing data using a widely-employed multiple 
imputation approach.61 This method replaces the missing data with a set of m possible 
values by creating m imputed datasets through Monte Carlo technique. Each imputed 
dataset was analyzed using the same methods that are used for complete datasets. 
Results were pooled together to provide valid statistical inferences.61 These methods 
enabled us to use data from the entire study sample, including patients with differential 
follow-up, maximizing the study’s statistical power to detect differences among 

treatment groups. Lastly, we conducted sensitivity analyses to examine the impact of 
the missing data on study results.62–65  
5. Cluster analysis: We examined for possible clustering within our study sample as data 
may have been clustered at several levels. For example, patients treated by the same 
physicians or facilities may have correlated outcomes, and patients living in the same 
area may share characteristics related to treatment outcomes. To account for clustering 
in our analysis of DFS (Aim 2), we conducted proportional hazard model using a robust 
sandwich estimate6> and likelihood-based random effect (frailty) model.67 For 
outcomes analyzed by linear and logistic regression models, we used GEE modeling 
approaches to obtain appropriate standard errors, and/or applied multilevel modeling 
to explicitly model the clustered data structures and allow for random intercepts of the 
higher level data structure.   
 
Evaluation of Treatment Outcome Heterogeneity: Our rich cohort comprised a sizable 
proportion of understudied populations, including patients of racial/ethnic minorities, lower 
socioeconomics, and rural/underserved areas. Therefore, we sought to assess heterogeneity of 
treatment effect (HTE) following PCORI guidelines. We performed descriptive HTE analysis to 
produce effect estimates and standard errors for key confounders (e.g., race and ethnicity, age, 
disease severity). We also explored subgroup analyses when sample size permitted enough 
power to detect meaningful differences.