Matsuyama Yutaka, Morita Satoshi
Department of Biostatistics, School of Health Sciences and Nursing, University of Tokyo, Tokyo, Japan.
Clin Trials. 2006;3(1):1-9. doi: 10.1191/1740774506cn135oa.
In clinical trials, when comparing treatments in a subgroup of patients defined by an event that occurred after randomization is required, the standard estimator that adjusts for the post-treatment variable does not have a causal interpretation.
To address this problem, we formulate clinically relevant causal estimands using the principal stratification framework developed by Frangakis and Rubin, and propose a new estimation method for the principal causal effect.
We consider the comparison of the duration of response among patients who responded to chemotherapy in a cancer clinical trial. Our goal is to estimate the local average treatment effect, that is, the treatment difference among patients who would have responded to either treatment. In order to identify this estimand, we make the assumption that the value of the counterfactual indicator of response is independent of both the actual response status and the outcome variable of interest conditional on the covariates. The proposed estimator is a weighted average of the standard estimators for responders where weights are the probability that the response would have occurred had the patient received the other treatment.
The proposed method is applied to data from a randomized phase III clinical trial in patients with advanced non-small-cell lung cancer. The average difference for the duration of response among responders estimated by the proposed method and the standard one was 16.1 (days) and 9.5 (days), respectively. We also evaluate the performance of the proposed method through simulation studies, which showed that the proposed estimator was unbiased, while the standard one was largely biased.
We have developed an estimation method for the local average treatment effect. For any type of outcome variables, our estimator can be easily constructed and can be interpreted as the treatment effect among patients who would have had the event in either treatment group.
在临床试验中，当需要比较由随机分组后发生的事件所定义的患者亚组中的治疗方法时，针对治疗后变量进行调整的标准估计量没有因果解释。
为解决这一问题，我们使用弗兰加基斯和鲁宾开发的主分层框架来制定临床相关的因果估计量，并提出一种新的主因果效应估计方法。
我们考虑在一项癌症临床试验中对化疗有反应的患者的反应持续时间进行比较。我们的目标是估计局部平均治疗效应，即对任何一种治疗都会有反应的患者之间的治疗差异。为了确定这个估计量，我们假设在协变量的条件下，反应的反事实指标的值与实际反应状态和感兴趣的结果变量均无关。所提出的估计量是反应者的标准估计量的加权平均值，权重是患者接受另一种治疗时发生反应的概率。
所提出的方法应用于晚期非小细胞肺癌患者的一项随机III期临床试验的数据。通过所提出的方法和标准方法估计的反应者之间反应持续时间的平均差异分别为16.1（天）和9.5（天）。我们还通过模拟研究评估了所提出方法的性能，结果表明所提出的估计量是无偏的，而标准估计量存在很大偏差。
我们开发了一种局部平均治疗效应的估计方法。对于任何类型的结果变量，我们的估计量都可以很容易地构建，并且可以解释为在任何一个治疗组中都会发生该事件的患者之间的治疗效应。
