Shu Di, Webster-Clark Michael, Platt Robert W, Toh Sengwee
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.
Pharmacoepidemiol Drug Saf. 2023 Jan;32(1):56-59. doi: 10.1002/pds.5527. Epub 2022 Sep 2.
To conceptualize a particular target population and estimand for multi-site pharmacoepidemiologic studies within data networks and to analytically examine sample-standardization as a meta-analytic method compared with inverse-variance weighted meta-analyses.
The target population of interest is all and only all individuals from the data-contributing sites. Standardization, a general conditioning technique frequently employed for confounding control, was adopted to estimate the network-wide causal treatment effect. Specifically, the proposed sample-standardization yields a meta-analysis estimator, that is, a weighted summation of site-specific results, where the weight for a site is the proportion of its size in the entire network. This sample-standardization estimator was evaluated analytically in comparison to estimators from inverse-variance weighted fixed-effect and random-effects meta-analyses in terms of statistical consistency.
A proof is reported to justify the consistency of the sample-standardization estimator with and without treatment effect heterogeneity by site. Both inverse-variance weighted fixed-effect and random-effects meta-analyses were found to generally result in inconsistent estimators in the presence of treatment effect heterogeneity by site for this particular target population and estimand.
Sample-standardization is a valid approach to generate causal inference in multi-site studies when the target population comprises all and only all individuals within the network, even in the presence of heterogeneity of treatment effect by site. Multi-site studies should clearly specify the target population and estimand to help select the most appropriate meta-analytic methods.
在数据网络范围内对多中心药物流行病学研究的特定目标人群和估计量进行概念化，并与逆方差加权荟萃分析相比，对样本标准化作为一种荟萃分析方法进行分析检验。
感兴趣的目标人群是数据贡献站点的所有个体且仅为这些个体。采用标准化这一常用于控制混杂的一般条件技术来估计全网络的因果治疗效果。具体而言，所提出的样本标准化产生一个荟萃分析估计量，即特定站点结果的加权总和，其中某个站点的权重是其规模在整个网络中所占的比例。就统计一致性而言，将该样本标准化估计量与逆方差加权固定效应和随机效应荟萃分析的估计量进行了分析评估。
报告了一个证明，以证明无论各站点是否存在治疗效果异质性，样本标准化估计量均具有一致性。对于这个特定的目标人群和估计量，发现在各站点存在治疗效果异质性的情况下，逆方差加权固定效应和随机效应荟萃分析通常会导致不一致的估计量。
当目标人群仅由网络内的所有个体组成时，即使存在各站点治疗效果的异质性，样本标准化也是在多中心研究中进行因果推断的有效方法。多中心研究应明确指定目标人群和估计量，以帮助选择最合适的荟萃分析方法。
