Dahabreh Issa J, Robertson Sarah E, Tchetgen Eric J, Stuart Elizabeth A, Hernán Miguel A
Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, Rhode Island.
Departments of Health Services, Policy & Practice and Epidemiology, Brown University, Providence, Rhode Island.
Biometrics. 2019 Jun;75(2):685-694. doi: 10.1111/biom.13009. Epub 2019 Jun 21.
We consider methods for causal inference in randomized trials nested within cohorts of trial-eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite-sample performance in simulation studies. As an illustration, we apply the estimators in a trial nested within a cohort of trial-eligible individuals to compare coronary artery bypass grafting surgery plus medical therapy vs. medical therapy alone for chronic coronary artery disease.
我们考虑在符合试验条件个体的队列中进行的随机试验中的因果推断方法，包括未被随机分组的个体。我们展示了如何利用来自整个队列的基线协变量数据，以及仅来自随机分组个体的治疗和结局数据，来识别所有符合条件个体的目标人群中的潜在（反事实）结局均值和平均治疗效果。我们回顾了可识别性条件，提出了估计量，并在模拟研究中评估了估计量的有限样本性能。作为一个示例，我们将这些估计量应用于一个嵌套在符合试验条件个体队列中的试验，以比较冠状动脉旁路移植术加药物治疗与单纯药物治疗对慢性冠状动脉疾病的疗效。
