To maximize clinical benefit, clinicians routinely tailor treatment to the individual characteristics of each patient, where individualized treatment rules are needed and are of significant research interest to statisticians. In the covariate-adjusted randomization clinical trial with many covariates, we model the treatment effect with an unspecified function of a single index of the covariates and leave the baseline response completely arbitrary. We devise a class of estimators to consistently estimate the treatment effect function and its associated index while bypassing the estimation of the baseline response, which is subject to the curse of dimensionality. We further develop inference tools to identify predictive covariates and isolate effective treatment region. The usefulness of the methods is demonstrated in both simulations and a clinical data example.
翻译:为了尽量扩大临床效益,临床医生经常根据每个病人的个别特征进行治疗,需要个别化的治疗规则,统计人员对此有重大研究兴趣。在同变调整随机化临床试验中,我们用许多共变的临床试验,用一个单一的共变指数的未说明的功能来模拟治疗效果,使基线反应变得完全任意。我们设计了一组测算员,以一致估计治疗效果功能及其相关指数,同时绕过基线反应的估算,而基线反应是受维度诅咒的。我们进一步开发推论工具,以确定预测性共变和分离的有效治疗区域。这些方法的有用性在模拟和临床数据实例中都得到了证明。