From Five‐Number Summary to Absolute Heterogeneity: Recent Methodological Advances in Meta‐Analysis With Continuous Outcomes

Abstract

Meta-analysis with continuous outcomes presents a range of methodological challenges. Among these, two issues have received increasing attention: (i) integrating studies that report only the five-number summary (such as the median, interquartile range, and range) rather than the sample mean and standard deviation (SD), and (ii) accurately quantifying between-study heterogeneity. This review first summarizes recent advances in estimating the sample mean and SD from the five-number summary, covering both normality- and non-normality-based estimation methods. We also review recently developed skewness tests that help determine when normality-based estimators are appropriate and present a practical flow chart for integrating studies with five-number summaries into meta-analysis. Building on this, we discuss methods for quantifying the heterogeneity, focusing on the widely used relative heterogeneity statistic and its limitations, particularly its dependence on study sample sizes. We then review the absolute heterogeneity statistic , which quantifies population-level variation across studies and is invariant to study sample sizes, thus complementing traditional measures. By synthesizing these methodological developments and providing practical guidelines and tools, this review aims to support more rigorous and transparent meta-analytic practice for continuous outcomes, especially in the presence of nonstandard reporting formats and varying degrees of heterogeneity.