Using Bayesian hierarchical models for controlled post hoc subgroup analysis of clinical trials: application to smoking cessation treatment in American Indians and Alaska Natives.
Elena Shergina, Kimber P Richter, Christine Makosky Daley, Babalola Faseru, Won S Choi, Byron J Gajewski
Author Information
Elena Shergina: Department of Biostatistics & Data Science, University of Kansas Cancer Center, Kansas City, Kansas, USA. ORCID
Kimber P Richter: Department of Population Health, University of Kansas Medical Center, Kansas City, Kansas, USA. ORCID
Christine Makosky Daley: Department of Community and Health Population, Lehigh University, Bethlehem, Pennsylvania, USA.
Babalola Faseru: Department of Population Health, University of Kansas Medical Center, Kansas City, Kansas, USA. ORCID
Won S Choi: Department of Community and Health Population, Lehigh University, Bethlehem, Pennsylvania, USA.
Byron J Gajewski: Department of Biostatistics & Data Science, University of Kansas Cancer Center, Kansas City, Kansas, USA. ORCID
Clinical trials powered to detect subgroup effects provide the most reliable data on heterogeneity of treatment effect among different subpopulations. However, pre-specified subgroup analysis is not always practical and post hoc analysis results should be examined cautiously. Bayesian hierarchical modelling provides grounds for defining a controlled post hoc analysis plan that is developed after seeing outcome data for the population but before unblinding the outcome by subgroup. Using simulation based on the results from a tobacco cessation clinical trial conducted among the general population, we defined an analysis plan to assess treatment effect among American Indians and Alaska Natives (AI/AN) enrolled in the study. Patients were randomized into two arms using Bayesian adaptive design. For the opt-in arm, clinicians offered a cessation treatment plan after verifying that a patient was ready to quit. For the opt-out arm, clinicians provided all participants with free cessation medications and referred them to a Quitline. The study was powered to test a hypothesis of significantly higher quit rates for the opt-out arm at one-month post randomization. Overall, one-month abstinence rates were 15.9% and 21.5% (opt-in and opt-out arm, respectively). For AI/AN, one-month abstinence rates were 10.2% and 22.0% (opt-in and opt-out arm, respectively). The posterior probability that the abstinence rate in the treatment arm is higher is 0.96, indicating that AI/AN demonstrate response to treatment at almost the same probability as the whole population.
