Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy

Zhao, Yuxi Lin Lifeng

Systematic reviews and meta-analyses have been increasingly used to pool research findings from multiple studies in medical sciences. The reliability of the synthesized evidence depends highly on the methodological quality of a systematic review and meta-analysis. In recent years, several tools have been developed to guide the reporting and evidence appraisal of systematic reviews and meta-analyses, and much statistical effort has been paid to improve their methodological quality. Nevertheless, many contemporary meta-analyses continue to employ conventional statistical methods, which may be suboptimal compared with several alternative methods available in the evidence synthesis literature. Based on a recent systematic review on COVID-19 in pregnancy, this article provides an overview of select good practices for performing meta-analyses from statistical perspectives. Specifically, we suggest meta-analysts (1) providing sufficient information of included studies, (2) providing information for reproducibility of meta-analyses, (3) using appropriate terminologies, (4) double-checking presented results, (5) considering alternative estimators of between-study variance, (6) considering alternative confidence intervals, (7) reporting prediction intervals, (8) assessing small-study effects whenever possible, and (9) considering one-stage methods. We use worked examples to illustrate these good practices. Relevant statistical code is also provided. The conventional and alternative methods could produce noticeably different point and interval estimates in some meta-analyses and thus affect their conclusions. In such cases, researchers should interpret the results from conventional methods with great caution and consider using alternative methods.