协变量
估计员
缺少数据
统计
人口
反事实思维
计算机科学
全国健康与营养检查调查
计量经济学
样本量测定
聚类分析
数据挖掘
医学
数学
心理学
社会心理学
环境卫生
作者
Jon A. Steingrimsson,David H. Barker,Ruofan Bie,Issa J Dahabreh
出处
期刊:Biostatistics
[Oxford University Press]
日期:2023-03-28
卷期号:25 (2): 289-305
被引量:1
标识
DOI:10.1093/biostatistics/kxad006
摘要
Summary Causally interpretable meta-analysis combines information from a collection of randomized controlled trials to estimate treatment effects in a target population in which experimentation may not be possible but from which covariate information can be obtained. In such analyses, a key practical challenge is the presence of systematically missing data when some trials have collected data on one or more baseline covariates, but other trials have not, such that the covariate information is missing for all participants in the latter. In this article, we provide identification results for potential (counterfactual) outcome means and average treatment effects in the target population when covariate data are systematically missing from some of the trials in the meta-analysis. We propose three estimators for the average treatment effect in the target population, examine their asymptotic properties, and show that they have good finite-sample performance in simulation studies. We use the estimators to analyze data from two large lung cancer screening trials and target population data from the National Health and Nutrition Examination Survey (NHANES). To accommodate the complex survey design of the NHANES, we modify the methods to incorporate survey sampling weights and allow for clustering.
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