The SMART-of-1 approach to personalized treatment trials

Adapting treatments to individual patient attributes has become essential in personalized healthcare, especially for managing complex and variable chronic diseases. Despite their important role in precision medicine, traditional N-of-1 trials face design challenges related to carryover effects, washout periods, and blinding. To address these limitations, we propose a novel design, which we call SMART-of-1, that incorporates elements from a small sample Sequential, Multiple Assignment Randomized Trials (snSMART) into the N-of-1 trial. This innovative approach is tailored for comparative effectiveness studies in chronic diseases with acute presentations, such as asthma and sleep disorders. We propose a Bayesian joint stage individual model and a Bayesian hierarchical model for SMART-of-1 trials to estimate both individual treatment effects and population-level effects in a combined final analysis. In addition, the within-cycle tailored sequences of treatments can be assessed in the binary outcome setting. Two case studies, a fatigue study and a sleep study, are presented to demonstrate the improved accuracy and efficiency of the SMART-of-1 design for different types of endpoints. In the fatigue study with a continuous endpoint, we demonstrate that both the estimated individual and aggregated treatment effects from the SMART-of-1 trials have improved efficiency in terms of bias and rMSE compared to a standard N-of-1 trial. Simulations from the sleep study indicate the improved estimation of cycle-wise dynamic treatment regimen (DTR) effects for the SMART-of-1 model compared to a conventional snSMART approach, particularly in the presence of heterogeneity among individual treatment effects.