Assessing study methodological quality and integrating this data into meta-analysis

Purpose of review Results of meta-analyses are frequently used to inform clinical practice guidelines and healthcare policy. However, healthcare recommendations derived from these meta-analyses may not be trustworthy if based on the results of biased studies. This literature review aims to provide an up-to-date summary of the state-of-the-art methods for integrating methodological quality data into meta-analyses, also known as bias adjustment, as well as the strengths and weaknesses of current methods. This is essential to ensure meta-analyses are conducted in a way that produces trustworthy and valid results. Recent findings This literature review outlines the various bias adjustment methods and some of the advantages and limitations of each. Quality effects modelling has emerged as a promising option, with few limitations and ease of application for meta-analysts. Summary This paper outlines what systematic reviewers can expect from different bias adjustment methods, which will be helpful in minimizing the impact of bias on study results and increasing the validity and reliability of findings from meta-analysis.