Abstract
BackgroundStatistical 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.MethodsThree 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.ResultsFor 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.ConclusionsStatistical 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.
Highlights
Statistical adjustment is often considered to control confounding bias in observational studies, especially case–control studies
The simulation assumed that all generated case–control studies were free from selection bias and information bias, and confounding bias was the major cause of systematic error that need to be carefully examined
When no covariates were adjusted in original case–control studies, the average effect estimation of meta-analyses was 2.82, which
Summary
Statistical adjustment is often considered to control confounding bias in observational studies, especially case–control studies. Meta-analysis is a well-developed statistical methodology to synthesize results of multiple original studies [1]. Since it increases the sample size for a specific research question by combining data from different independent studies, meta-analysis enhances the accuracy of effect estimation and improves the strength of evidence [2]. Compared with RCTs, observational studies, especially case–control studies, are exposed to several potential risk of bias which may bring systematic errors in effect estimations [9]. When conducting meta-analyses of case–control studies, all potential bias of original studies should be properly addressed [10, 11]. Logistic regression model is one of the most widely used approaches to control multiple confounders simultaneously, and odds ratio (OR) is a common estimator of causal effect
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