Comparison of bias adjustment methods in meta-analysis suggests that quality effects modeling may have less limitations than other approaches

Background: The quality of primary research is commonly assessed before inclusion in meta-analyses. Findings are discussed in the context of the quality appraisal by categorizing studies according to risk of bias. The impact of appraised risk of bias on study outcomes is typically judged by the reader; however, several methods have been developed to quantify this risk of bias assessment and incorporate it into the pooled results of meta-analysis, a process known as bias adjustment. The advantages, potential limitations, and applicability of these methods are not well defined. Study Design and Setting: Comparative evaluation of the applicability of the various methods and their limitations are discussed using two examples from the literature. These methods include weighting, stratification, regression, use of empirically based prior distributions, and elicitation by experts. Results: Use of the two examples from the literature suggest that all methods provide similar adjustment. Methods differed mainly in applicability and limitations. Conclusion: Bias adjustment is a feasible process in meta-analysis with several strategies currently available. Quality effects modelling was found to be easily implementable with fewer limitations in comparison to other methods. (C) 2019 Elsevier Inc. All rights reserved.