Identification and estimation of treatment effects on long-term outcomes in clinical trials with external observational data
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结合外部观测数据识别与估计临床试验长期处理效应
- 研究问题
- 当随机试验缺少长期结局、外部观测研究虽记录长期结局却可能存在未测混杂时，如何识别试验人群的长期平均处理效应？
- 方法与思路
- 作者结合两类数据，在均值可交换性假设下识别效应，推导有效影响函数，并提出局部有效双重稳健与逆概率加权估计。
- 材料陈述的贡献
- 摘要称建立效率界与估计量渐近性质；模型正确时双重稳健估计达到效率界，有限支持离散情形下所述加权估计亦可达到。
- 阅读关注点
- 应核对跨研究均值可交换性的内容、双重稳健对应的模型组合，以及估计倾向得分提高效率和达到效率界的条件。
- 阅读边界
- 所据为 arXiv 预印本，同行评审或期刊接收未经核实；摘要未列完整识别条件，药物应用结果也不足以据此判断临床疗效。
原文摘要
In biomedical studies, estimating drug effects on chronic diseases requires a long follow-up period, which is difficult to meet in randomized clinical trials (RCTs). The use of a short-term surrogate to replace the long-term outcome for assessing the drug effect relies on stringent assumptions that empirical studies often fail to satisfy. Motivated by a kidney disease study, we investigate the drug effects on long-term outcomes by combining an RCT without observation of long-term outcome and an observational study in which the long-term outcome is observed but unmeasured confounding may exist. Under a mean exchangeability assumption weaker than the previous literature, we identify the average treatment effects in the RCT and derive the associated efficient influence function and semiparametric efficiency bound. Furthermore, we propose a locally efficient doubly robust estimator and an inverse probability weighted (IPW) estimator. The former attains the semiparametric efficiency bound if all the working models are correctly specified. The latter has a simpler form and requires much fewer model specifications. The IPW estimator using estimated propensity scores is more efficient than that using true propensity scores and achieves the semiparametric efficient bound in the case of discrete covariates and surrogates with finite support. Both estimators are shown to be consistent and asymptotically normally distributed. Extensive simulations are conducted to evaluate the finite-sample performance of the proposed estimators. We apply the proposed methods to estimate the efficacy of oral hydroxychloroquine on renal failure in a real-world data analysis.