CO2.3 - Optimal treatment regimes for the net benefit of a treatment

One aim of personalized medicine is to identify which treatment among several options is the most beneficial for individual patients. For that purpose, several methods have been developed to construct individualized treatment rules (ITR). An ITR provides a tailored treatment recommendation to each patient, based on their observed characteristics to improve their clinical outcome. Nonetheless, most statistical methods used to construct ITRs only consider a single outcome, while clinical evaluation is usually more complex. For instance, overall survival and progression-free survival are both commonly used as clinical endpoints in oncology. These multiple clinical endpoints should inform the clinicians and patients on how a potential treatment may improve survival, relieve symptoms, and affect the quality of life. As the finality of an ITR is to propose personalized treatment recommendations, these multiple clinical endpoints and their relations should be taken into account when constructing an ITR. Objective: To devise statistical techniques for constructing ITRs that consider multiple clinical endpoints simultaneously and can handle censored outcomes. We developed a mathematical setup relying on Rubin's causal model and inspired by Buyse's generalized pairwise comparisons to define the notion of an optimal ITR in the presence of a hierarchy of clinical endpoints, terming such an ITR pairwise optimal. We present two approaches to estimate pairwise optimal ITRs. The first is a variant of the k-nearest neighbors algorithm. The second is a meta-learner based on a randomized bagging scheme, allowing the use of any classification algorithm for constructing an individualized treatment rule. We study the behavior of these estimation schemes from a theoretical standpoint and through Monte Carlo simulations, and illustrate their use on the POPLAR and OAK trials data. We proved that the two estimation schemes we proposed are universally consistent and that pairwise optimal rules are optimal in the usual sense in the case of a single binary endpoint. We have developed new methods that allow to consider simultaneously several endpoints and use censored data when designing ITR. Our simple method extends to personalized medicine Buyse's generalized pairwise comparison method.