Abstract C013: Importance of statistical considerations in multi-center, transatlantic cancer disparity studies: Lessons learned from the CaPTC prostate cancer familial cohort study
Authors: Lee Daniel , Investigators CaPTC , Crook Jennifer , Enuka Ruth , Parisa Fathi , Iweala Emeka Iweala , Kukoyi Oluwole , Kaninjing Ernest , Toye Oluwaseyi , Jibrin Paul , Odedina Folakemi 
Published: December 2023

Abstract
The CaPTC prostate cancer (CaP) Familial Cohort study (CaPFCS) was launched in 2017 to explore the CaP risk factors of African Black men (ABM). The study design is a prospective study that observes and follows healthy ABM and ABM diagnosed with CaP. Aim: The specific aim is to provide recommendations on statistical considerations for cohort studies focused on cancer disparities, based on lessons learned from the CaPTC CaPFCS. Methodology: Study participants were ABM in Cameroon, Nigeria, and the US. Using validated measures, data were collected mostly by self-administered or research assistant administered survey using paper survey (especially in Cameroon and Nigeria) with the responses transferred to REDCap by research coordinators. The statistical analysis for the CaPTC CaPFCS focused on the baseline epidemiological, behavioral, clinical and anthropometric measures of Black men. A total of 819 participants were included in the analyses. Statistical analyses included descriptive statistics (sample median, range, frequency and percentages) and unadjusted logistic regression models. Results: Data collection for the CaPTC CaPFCS were by multiple investigators/research coordinators at multiple sites in Cameroon, Nigeria, and the US. In preparation for the data analyses, we noted data entry errors and/or misinterpretation of data entry, which required extensive cleaning of the database prior to the analyses. The biostatisticians spent significant effort cleaning and ensuring data validity prior to the analyses. Confounding variables is another statistical consideration for cohort studies, such as the CaPTC CaPFCS. There was variability observed across age, education levels, employment status, household income and CaP diagnosis based on country of residence. CaP diagnosis was not reported by any of the ABM in the US, which may be due to the fact that they were all recruited within the community and no recruitment took place in the clinic. Finally, the CaPTC CaPFCS has several variables. Performing a lot of statistical tests may result in false-positive findings at P&lt;0.05.Conclusion: The advantage of the CaPFCS is that it allows the CaPTC investigators to study epidemiological, behavioral, clinical and anthropometric variables associated with CaP over time. Being a longitudinal cohort study, investigators can track the changes in exposure and outcomes over time.However, statistical considerations need to be at the fore-front of multi-center, transatlantic cancer disparity studies such as the CaPTC CaPFCS, from data collection to data entry. The use of web-based survey with the ability to enter data directly into REDCaP will significantly reduce (if not totally eliminate) the need for database cleaning. There are multiple strategies that can be used to reduce confounder effect, including statistical adjustment and multivariate analysis to adjust for the confounders. The use of propensity score methods have especially been found to be effective in controlling baseline confounding factors.
Citation Format: Daniel Lee, CaPTC Investigators, Jennifer Crook, Ruth Enuka, Fathi Parisa, Emeka Iweala Iweala, Oluwole Kukoyi, Ernest Kaninjing, Oluwaseyi Toye, Paul Jibrin, Folakemi Odedina. Importance of statistical considerations in multi-center, transatlantic cancer disparity studies: Lessons learned from the CaPTC prostate cancer familial cohort study [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C013.