Standard error estimation in meta-analysis of studies reporting medians

Posted on 24.11.2022 - 03:10

We consider the setting of an aggregate data meta-analysis of a continuous outcome of interest. When the distribution of the outcome is skewed, it is often the case that some primary studies report the sample mean and standard deviation of the outcome and other studies report the sample median along with the first and third quartiles and/or minimum and maximum values. To perform meta-analysis in this context, a number of approaches have recently been developed to impute the sample mean and standard deviation from studies reporting medians. Then, standard meta-analytic approaches with inverse-variance weighting are applied based on the (imputed) study-specific sample means and standard deviations. In this article, we illustrate how this common practice can severely underestimate the within-study standard errors, which results in poor coverage for the pooled mean in common effect meta-analyses and overestimation of between-study heterogeneity in random effects meta-analyses. We propose a straightforward bootstrap approach to estimate the standard errors of the imputed sample means. Our simulation study illustrates how the proposed approach can improve the estimation of the within-study standard errors and consequently improve coverage for the pooled mean in common effect meta-analyses and estimation of between-study heterogeneity in random effects meta-analyses. Moreover, we apply the proposed approach in a meta-analysis to identify risk factors of a severe course of COVID-19.

CITE THIS COLLECTION

McGrath, Sean; Katzenschlager, Stephan; Zimmer, Alexandra J; Seitel, Alexander; Steele, Russell; Benedetti, Andrea (2022): Standard error estimation in meta-analysis of studies reporting medians. SAGE Journals. Collection. https://doi.org/10.25384/SAGE.c.6314481.v1
or
Select your citation style and then place your mouse over the citation text to select it.

SHARE

email
need help?