One-sample aggregate data meta-analysis of medians

TL;DR

This paper introduces and compares two new median-based methods for meta-analyzing continuous outcomes reported as medians, demonstrating their superior performance over transformation methods, especially with skewed data.

Contribution

The paper proposes two novel median-based meta-analysis approaches and systematically compares them to existing transformation methods through simulations and real data application.

Findings

Median-based methods outperform transformation approaches with skewed data.

Median-based approaches are comparable or better than using actual means in meta-analysis.

Proposed methods are effective for skewed data and high inter-study variance.

Abstract

An aggregate data meta-analysis is a statistical method that pools the summary statistics of several selected studies to estimate the outcome of interest. When considering a continuous outcome, typically each study must report the same measure of the outcome variable and its spread (e.g., the sample mean and its standard error). However, some studies may instead report the median along with various measures of spread. Recently, the task of incorporating medians in meta-analysis has been achieved by estimating the sample mean and its standard error from each study that reports a median in order to meta-analyze the means. In this paper, we propose two alternative approaches to meta-analyze data that instead rely on medians. We systematically compare these approaches via simulation study to each other and to methods that transform the study-specific medians and spread into sample means and their standard errors. We demonstrate that the proposed median-based approaches perform better than the transformation-based approaches, especially when applied to skewed data and data with high inter-study variance. In addition, when meta-analyzing data that consists of medians, we show that the median-based approaches perform considerably better than or comparably to the best-case scenario for a transformation approach: conducting a meta-analysis using the actual sample mean and standard error of the mean of each study. Finally, we illustrate these approaches in a meta-analysis of patient delay in tuberculosis diagnosis.