Center for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Nanlishilu 56, Xicheng District, Beijing, 100045, China.
Evidence Generation, Medical Affairs, AstraZenaca, Level 22, International Fortune Center, Jianguomenwai Avenue 8, Chaoyang District, Beijing, 100010, China.
BMC Med Inform Decis Mak. 2020 Dec 11;20(1):333. doi: 10.1186/s12911-020-01343-3.
Statistical adjustment is often considered to control confounding bias in observational studies, especially case-control studies. However, different adjustment strategies may affect the estimation of odds ratios (ORs), and in turn affect the results of their pooled analyses. Our study is aimed to investigate how to deal with the statistical adjustment in case-control studies to improve the validity of meta-analyses.
Three types of adjustment strategies were evaluated including insufficient adjustment (not all preset confounders were adjusted), full adjustment (all confounders were adjusted under the guidance of causal inference), and improper adjustment (covariates other than confounders were adjusted). We carried out a series of Monte Carlo simulation experiments based on predesigned scenarios, and assessed the accuracy of effect estimations from meta-analyses of case-control studies by combining ORs calculated according to different adjustment strategies. Then we used the data from an empirical review to illustrate the replicability of the simulation results.
For all scenarios with different strength of causal relations, combining ORs that were comprehensively adjusted for confounders would get the most precise effect estimation. By contrast, combining ORs that were not sufficiently adjusted for confounders or improperly adjusted for mediators or colliders would easily introduce bias in causal interpretation, especially when the true effect of exposure on outcome was weak or none. The findings of the simulation experiments were further verified by the empirical research.
Statistical adjustment guided by causal inference are recommended for effect estimation. Therefore, when conducting meta-analyses of case-control studies, the causal relationship formulated by exposure, outcome, and covariates should be firstly understood through a directed acyclic graph, and then reasonable original ORs could be extracted and combined by suitable methods.
统计调整通常被认为可以控制观察性研究（尤其是病例对照研究）中的混杂偏差。然而，不同的调整策略可能会影响比值比（OR）的估计，进而影响其汇总分析的结果。我们的研究旨在探讨如何处理病例对照研究中的统计调整，以提高荟萃分析的有效性。
评估了三种调整策略，包括不充分调整（未调整所有预设混杂因素）、完全调整（在因果推理的指导下调整所有混杂因素）和不当调整（调整混杂因素以外的协变量）。我们根据预设的情景进行了一系列蒙特卡罗模拟实验，并通过结合根据不同调整策略计算的 OR 来评估病例对照研究荟萃分析中效应估计的准确性。然后，我们使用实证综述的数据来说明模拟结果的可重复性。
对于因果关系强度不同的所有情景，结合全面调整混杂因素的 OR 可以获得最精确的效应估计。相比之下，结合未充分调整混杂因素或不当调整中介因素或共发因素的 OR 容易导致因果解释出现偏差，尤其是当暴露对结局的真实效应较弱或不存在时。模拟实验的结果进一步通过实证研究得到验证。
建议根据因果推理进行统计调整以进行效应估计。因此，在进行病例对照研究的荟萃分析时，应首先通过有向无环图理解暴露、结局和协变量之间的因果关系，然后通过合适的方法提取和合并合理的原始 OR。
