A Partially Randomized Patient Preference, Sequential, Multiple-Assignment, Randomized Trial Design Analyzed via Weighted and Replicated Frequentist and Bayesian Methods

Results from randomized control trials (RCTs) may not be representative when individuals refuse to be randomized or are excluded for having a preference for which treatment they receive. If trial designs do not allow for participant treatment preferences, trials can suffer in accrual, adherence, retention, and external validity of results. Thus, there is interest surrounding clinical trial designs that incorporate participant treatment preferences. We propose a Partially Randomized, Patient Preference, Sequential, Multiple Assignment, Randomized Trial (PRPP-SMART) which combines a Partially Randomized, Patient Preference (PRPP) design with a Sequential, Multiple Assignment, Randomized Trial (SMART) design. This novel PRPP-SMART design is a multi-stage clinical trial design where, at each stage, participants either receive their preferred treatment, or if they do not have a preferred treatment, they are randomized. This paper focuses on the clinical trial design for PRPP-SMARTs and the development of Bayesian and frequentist weighted and replicated regression models (WRRMs) to analyze data from such trials. We propose a two-stage PRPP-SMART with binary end of stage outcomes and estimate the embedded dynamic treatment regimes (DTRs). Our WRRMs use data from both randomized and non-randomized participants for efficient estimation of the DTR effects. We compare our method to a more traditional PRPP analysis which only considers participants randomized to treatment. Our Bayesian and frequentist methods produce more efficient DTR estimates with negligible bias despite the inclusion of non-randomized participants in the analysis. The proposed PRPP-SMART design and analytic method is a promising approach to incorporate participant treatment preferences into clinical trial design.