A Principal Stratification Approach to Estimating the Effect of Continuing Treatment after Observing Early Outcomes
提出一种利用随机对照试验数据估计继续治疗长期效果的方法，通过主分层分析处理早期结果导致的治疗中断问题，并以加波沙朵治疗失眠为例进行说明。
Abstract Chronic diseases often require continuing care, and early response to treatment can be an important predictor of long-term efficacy. Often, an apparent lack of early efficacy may lead to discontinuation of treatment, with the decision made either by clinicians or by the patients themselves. Thus, it is important to determine whether or not a desired early outcome corresponds to a beneficial long-term effect of continuing treatment, and conversely, whether or not the absence of such an outcome corresponds to a lack of long-term benefit. However, primary clinical trials of such treatments are not commonly designed to answer such questions, for example by randomizing subjects to continue or discontinue treatment after observing early outcomes. We propose an approach to estimating the effect of continuing treatment after observing early outcomes using data from randomized controlled trials in which treatment discontinuation was not part of the design. Our approach estimates average causal effects of continuing treatment on long-term outcomes in principal strata defined by the potential early outcomes under treatment. For illustration, we estimate the effects of continuing to take gaboxadol to treat insomnia conditional on early improvement in subjective sleep quality after two nights, based on a standard parallel-arm randomized controlled trial.