Combining meta-analysis with multiple imputation for one-step, privacy-protecting estimation of causal treatment effects in multi-site studies

IF 5 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Research Synthesis Methods Pub Date : 2023-08-01 DOI:10.1002/jrsm.1660
Di Shu, Xiaojuan Li, Qoua Her, Jenna Wong, Dongdong Li, Rui Wang, Sengwee Toh
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Abstract

Missing data complicates statistical analyses in multi-site studies, especially when it is not feasible to centrally pool individual-level data across sites. We combined meta-analysis with within-site multiple imputation for one-step estimation of the average causal effect (ACE) of a target population comprised of all individuals from all data-contributing sites within a multi-site distributed data network, without the need for sharing individual-level data to handle missing data. We considered two orders of combination and three choices of weights for meta-analysis, resulting in six approaches. The first three approaches, denoted as RR + metaF, RR + metaR and RR + std, first combined results from imputed data sets within each site using Rubin's rules and then meta-analyzed the combined results across sites using fixed-effect, random-effects and sample-standardization weights, respectively. The last three approaches, denoted as metaF + RR, metaR + RR and std + RR, first meta-analyzed results across sites separately for each imputation and then combined the meta-analysis results using Rubin's rules. Simulation results confirmed very good performance of RR + std and std + RR under various missing completely at random and missing at random settings. A direct application of the inverse-variance weighted meta-analysis based on site-specific ACEs can lead to biased results for the targeted network-wide ACE in the presence of treatment effect heterogeneity by site, demonstrating the need to clearly specify the target population and estimand and properly account for potential site heterogeneity in meta-analyses seeking to draw causal interpretations. An illustration using a large administrative claims database is presented.

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结合荟萃分析与多重归算的一步,在多地点研究中保护隐私的因果治疗效果估计
缺少数据使多站点研究的统计分析复杂化,特别是当无法集中汇集跨站点的个人数据时。我们将荟萃分析与站点内多重代入相结合,对目标人群的平均因果效应(ACE)进行一步估计,目标人群由来自多站点分布式数据网络中所有数据提供站点的所有个体组成,而无需共享个人层面的数据来处理缺失数据。我们考虑了两种组合顺序和三种权重选择进行meta分析,结果有六种方法。前三种方法分别为RR + metaF、RR + metaR和RR + std,首先使用Rubin规则将每个站点内输入数据集的结果组合起来,然后分别使用固定效应、随机效应和样本标准化权重对组合结果进行meta分析。后三种方法分别为metaF + RR、metaR + RR和std + RR,首先对每个imputation进行跨站点的meta分析结果,然后使用Rubin规则将meta分析结果合并。仿真结果证实了RR + std和std + RR在各种完全随机缺失和随机缺失设置下都有很好的性能。直接应用基于特定地点ACE的反方差加权荟萃分析可能会导致在不同地点存在治疗效果异质性的情况下,针对目标网络范围ACE的结果存在偏差,这表明在寻求因果解释的荟萃分析中,需要明确指定目标人群,并估计和适当考虑潜在的地点异质性。给出了一个使用大型行政索赔数据库的实例。
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来源期刊
Research Synthesis Methods
Research Synthesis Methods MATHEMATICAL & COMPUTATIONAL BIOLOGYMULTID-MULTIDISCIPLINARY SCIENCES
CiteScore
16.90
自引率
3.10%
发文量
75
期刊介绍: Research Synthesis Methods is a reputable, peer-reviewed journal that focuses on the development and dissemination of methods for conducting systematic research synthesis. Our aim is to advance the knowledge and application of research synthesis methods across various disciplines. Our journal provides a platform for the exchange of ideas and knowledge related to designing, conducting, analyzing, interpreting, reporting, and applying research synthesis. While research synthesis is commonly practiced in the health and social sciences, our journal also welcomes contributions from other fields to enrich the methodologies employed in research synthesis across scientific disciplines. By bridging different disciplines, we aim to foster collaboration and cross-fertilization of ideas, ultimately enhancing the quality and effectiveness of research synthesis methods. Whether you are a researcher, practitioner, or stakeholder involved in research synthesis, our journal strives to offer valuable insights and practical guidance for your work.
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