Natural heterogeneity in patient populations can make it very hard to develop treatments that benefit all patients. As a result, an important goal of precision medicine is identification of patient subgroups that respond to treatment at a much higher (or lower) rate than the population average. Despite there being many subgroup identification methods, there is no comprehensive comparative study of their statistical properties. We review 13 methods and use real‐world and simulated data to compare the performance of their publicly available software using seven criteria: (a) bias in selection of subgroup variables, (b) probability of false discovery, (c) probability of identifying correct predictive variables, (d) bias in estimates of subgroup treatment effects, (e) expected subgroup size, (f) expected true treatment effect of subgroups, and (g) subgroup stability. The results show that many methods fare poorly on at least one criterion.

中文翻译：

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精准医学的亚组鉴定：13种方法的比较回顾

患者人群的自然异质性使得很难开发出使所有患者受益的治疗方法。结果，精确医学的一个重要目标是确定对患者产生反应的亚组，该亚组的治疗率要比人口平均水平高得多（或更低）。尽管有许多亚组识别方法，但对其统计特性没有全面的比较研究。我们回顾了13种方法，并使用现实和模拟数据通过以下七个标准比较了其公开软件的性能：（a）在选择亚组变量时存在偏见，（b）错误发现的可能性，（c）识别正确的可能性预测变量，（d）对亚组治疗效果的估计偏差，（e）预期亚组规模，（f）预期亚组真实治疗效果，（g）亚组稳定性。结果表明，许多方法在至少一个标准上效果不佳。