How to Conduct a Bias-Reduced Meta-Analysis : A Digital Guide

Abstract

Meta-analysis is a powerful method for synthesizing evidence from multiple studies, but it is also prone to various sources of bias that can affect its validity and reliability. This guide aims to supplement existing guidelines on how to conduct a meta-analysis by focusing on the most common pitfalls that may lead to bias and how to avoid it. We provide an overview of potential sources of bias in meta-analysis, such as publication bias, selection bias, methodological bias, and reporting bias. We describe how not conducting a meta-analysis or not or improperly aggregating study data can result in biased conclusions. We focus on the three main steps of meta-analysis: literature search, study coding, and data synthesis. We explain how to conduct quality checks and reduce biases at each step of the meta-analysis process. We illustrate specific biases and their consequences with examples from the literature and show how to overcome them with appropriate methods and tools. Our guide is accompanied by reflection tasks to increase the learning output and to help readers apply the principles of bias avoidance to their own meta-analyses. Our guide is not exhaustive and does not cover all possible sources of bias or methods of meta-analysis. Readers are encouraged to consult and pointed to other sources for more detailed and comprehensive guidance. The chapter serves as an ideal preparation for critically reading and conducting meta-analyses without falling into the most common pitfalls that may lead to severe biases and misleading results.