Design considerations and analytical framework for reliably identifying a beneficial individualized treatment rule

An individualized treatment rule (ITR) formalizes personalized medicine by assigning treatment as a function of patients' clinical information, which contrasts with a static treatment rule that assigns everyone the same treatment. ITR identification has become a common aim in randomized clinical trials but sample size considerations for this aim are lacking. One approach is to select a sample size that will reliably identify an ITR with a performance close to the theoretical optimal rule. However, this approach could still lead to identifying ITRs that perform worse than the optimal static rule, particularly in the absence of substantial effect heterogeneity. This limitation motivates sample size considerations aimed at reliable identification of a beneficial ITR, which outperforms the optimal static rule, and analysis methods that identify the estimated optimal static rule when there is substantial uncertainty about whether an ITR will improve outcomes. To address these limitations, we propose a sample size approach based on the probability of identifying a beneficial ITR and introduce an approach for selecting the LASSO penalty parameter such that in the absence of treatment effect heterogeneity the estimated optimal static rule is identified with high probability. We apply these approaches to the PLUTO trial aimed at developing methods to assist with smoking cessation.