Integrating multiple observational studies to make unconfounded causal or descriptive comparisons of group potential outcomes in a large natural population is challenging. Moreover, retrospective cohorts, being convenience samples, are usually unrepresentative of the natural population of interest and have groups with unbalanced covariates. We propose a general covariate-balancing framework based on pseudo-populations that extends established weighting methods to the meta-analysis of multiple retrospective cohorts with multiple groups. Additionally, by maximizing the effective sample sizes of the cohorts, we propose a FLEXible, Optimized, and Realistic (FLEXOR) weighting method appropriate for integrative analyses. We develop new weighted estimators for unconfounded inferences on wide-ranging population-level features and estimands relevant to group comparisons of quantitative, categorical, or multivariate outcomes. Asymptotic properties of these estimators are examined. Through simulation studies and meta-analyses of TCGA datasets, we demonstrate the versatility and reliability of the proposed weighting strategy, especially for the FLEXOR pseudo-population.

翻译：整合多个观察性研究以对大型自然人群中的组别潜在结果进行无混杂的因果或描述性比较具有挑战性。此外，回顾性队列作为便利样本，通常不能代表所关注的自然人群，并且其组间协变量不平衡。我们提出了一种基于伪人群的通用协变量平衡框架，该框架将成熟的加权方法扩展到具有多组别的多个回顾性队列的元分析中。此外，通过最大化各队列的有效样本量，我们提出了一种适用于整合分析的FLEXible、Optimized、and Realistic（FLEXOR）加权方法。我们开发了新的加权估计量，用于对与定量、分类或多元结果的组间比较相关的广泛人群水平特征和估计量进行无混杂推断。我们检验了这些估计量的渐近性质。通过模拟研究和对TCGA数据集的元分析，我们证明了所提出的加权策略（尤其是FLEXOR伪人群）的多功能性和可靠性。