论文标题
仅使用试验摘要统计数据评估个性化治疗的潜在益处吗？
Can the potential benefit of individualizing treatment be assessed using trial summary statistics alone?
论文作者
论文摘要
个性化治疗分配可以改善比较治疗效果中患者对患者变异性的疾病的预后。当一项临床试验表明某些患者对治疗有所改善，而另一些患者则没有改善治疗，这很容易假设存在治疗效应异质性。但是，如果反应中的变异性主要由治疗以外的其他因素驱动，则研究协变数据可以预测差异治疗反应的程度可能是资源的潜在浪费。由最近仅使用摘要统计数据评估对重度抑郁症的个性化治疗的潜在的荟萃分析的动机，我们提供了一种方法，该方法使用了在已发表的临床试验结果中广泛使用的摘要统计数据，以限制为每个患者最佳分配治疗的好处。我们还为设置提供了替代范围，在该设置中，试验结果由另一协变量分层。我们使用抑郁症治疗试验的摘要统计数据证明了我们的方法。我们的方法在RCT2OtrBounds R软件包中实现，该软件包可在https://github.com/ngalanter/rct2otrbounds上获得。
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 variability in response is mainly driven by factors other than treatment, 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. We demonstrate our approach using summary statistics from a depression treatment trial. Our methods are implemented in the rct2otrbounds R package, which is available at https://github.com/ngalanter/rct2otrbounds .
