Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA 98195, United States.
Department of Health Care Policy, Harvard Medical School, Boston, MA 02115, United States.
Am J Epidemiol. 2024 Aug 5;193(8):1161-1167. doi: 10.1093/aje/kwae040.
Individualizing treatment assignment can improve outcomes for diseases with patient-to-patient variability in comparative treatment effects. When a clinical trial demonstrates that some patients improve on treatment while others do not, it is tempting to assume that treatment effect heterogeneity exists. However, if outcome variability is mainly driven by factors other than variability in the treatment effect, investigating the extent to which covariate data can predict differential treatment response is a potential waste of resources. Motivated by recent meta-analyses assessing the potential of individualizing treatment for major depressive disorder using only summary statistics, we provide a method that uses summary statistics widely available in published clinical trial results to bound the benefit of optimally assigning treatment to each patient. We also offer alternate bounds for settings in which trial results are stratified by another covariate. Our upper bounds can be especially informative when they are small, as there is then little benefit to collecting additional covariate data. We demonstrate our approach using summary statistics from a depression treatment trial. Our methods are implemented in the rct2otrbounds R package.
对于治疗效果在患者间存在差异的疾病，采用个体化治疗分配可以改善治疗效果。当临床试验表明某些患者接受治疗后有所改善，而另一些患者则没有改善时，人们很容易假设存在治疗效果异质性。然而，如果结果的变异性主要是由治疗效果以外的因素驱动的，那么调查协变量数据在多大程度上可以预测治疗反应的差异，可能是一种浪费资源的做法。受最近使用仅汇总统计数据评估个体化治疗重度抑郁症的潜在效益的荟萃分析的启发，我们提供了一种方法，该方法使用发表的临床试验结果中广泛可用的汇总统计数据来限制为每个患者优化分配治疗的益处。我们还为按另一个协变量分层的试验结果提供了替代边界。当上限值较小时，这些上限值特别有用，因为此时收集额外协变量数据的好处很小。我们使用抑郁症治疗试验的汇总统计数据来演示我们的方法。我们的方法在 rct2otrbounds R 包中实现。
