One‐sample aggregate data meta‐analysis of medians

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 (eg, the sample m...

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Bibliographic Details
Published in:Statistics in medicine Vol. 38; no. 6; pp. 969 - 984
Main Authors: McGrath, Sean, Zhao, XiaoFei, Qin, Zhi Zhen, Steele, Russell, Benedetti, Andrea
Format: Journal Article
Language:English
Published: England Wiley Subscription Services, Inc 15.03.2019
Subjects:
aggregate data
Computer Simulation
Data Interpretation, Statistical
Delayed Diagnosis - statistics & numerical data
Humans
median
Medical research
Medical statistics
Meta-analysis
Meta-Analysis as Topic
Models, Statistical
Sampling
simulation study
skewed data
Statistical methods
Statistics as Topic - methods
Tuberculosis, Pulmonary - diagnosis
meta-analysis
median
simulation study
skewed data
aggregate data
ISSN:0277-6715, 1097-0258, 1097-0258
Online Access:Get full text
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Summary: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 (eg, 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. Finally, we illustrate these approaches in a meta‐analysis of patient delay in tuberculosis diagnosis.
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ISSN:0277-6715
1097-0258
1097-0258
DOI:10.1002/sim.8013