Optimal treatment regimes for the net benefit of a treatment

We developed a mathematical setup inspired by Buyse's generalized pairwise comparisons to define a notion of optimal individualized treatment rule (ITR) in the presence of prioritized outcomes in a randomized controlled trial, 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 ITR. We study the behavior of these estimation schemes from a theoretical standpoint and through Monte Carlo simulations and illustrate their use on trial data.