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.

翻译：我们受Buyse广义配对比较的启发，建立了一个数学框架，用于在随机对照试验中存在优先结局时定义个体化治疗规则（ITR）的最优性概念，并将此类ITR称为配对最优。我们提出了两种估计配对最优ITR的方法。第一种是k近邻算法的变体。第二种是基于随机装袋方案的元学习器，允许使用任何分类算法构建ITR。我们从理论角度并通过蒙特卡洛模拟研究了这些估计方案的行为，并在试验数据上展示了其应用。