Randomized controlled trials are often run in settings with many subpopulations that may have differential benefits from the treatment being evaluated. We consider the problem of sample selection, i.e., whom to enroll in a randomized trial, such as to optimize welfare in a heterogeneous population. We formalize this problem within the minimax-regret framework, and derive optimal sample-selection schemes under a variety of conditions. Using data from a COVID-19 vaccine trial, we also highlight how different objectives and decision rules can lead to meaningfully different guidance regarding optimal sample allocation.

翻译：随机对照试验常在存在多个亚群的情境下进行，这些亚群可能从所评估的治疗中获得不同的收益。我们考虑样本选择问题，即在随机试验中应招募哪些受试者，以优化异质人群的福利。我们在最小化最大遗憾框架内形式化了这一问题，并在多种条件下推导出最优的样本选择方案。利用一项COVID-19疫苗试验的数据，我们还展示了不同的目标与决策规则如何导致关于最优样本分配具有显著差异的指导。