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. The 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在大型自然人群中进行无混杂的因果或描述性组间潜在结局比较颇具挑战。此外，回顾性队列作为便利样本通常无法代表目标自然人群，且组间协变量分布不均衡。我们提出一种基于伪总体的通用协变量平衡框架，将现有加权方法扩展至多组多回顾性队列的元分析。通过最大化队列有效样本量，我们提出一种灵活、优化且现实（FLEXOR）的加权方法，适用于整合分析。我们开发了新的加权估计量，用于对定量、分类或多变量结局的组间比较所涉及的人群水平特征和估计量进行无混杂推断，并考察了这些估计量的渐近性质。通过模拟研究和TCGA数据集的元分析，我们展示了所提加权策略（尤其是FLEXOR伪总体）的通用性与可靠性。