Volume 62, Issue 1
RESEARCH PAPER

Meta‐analysis of the difference of medians

Sean McGrath

Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada

Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montreal, Quebec, Canada

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Hojoon Sohn

Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA

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Russell Steele

Department of Mathematics and Statistics, McGill University, Montreal, Quebec, Canada

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Andrea Benedetti

Corresponding Author

E-mail address: andrea.benedetti@mcgill.ca

Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montreal, Quebec, Canada

Department of Medicine, McGill University, Montreal, Quebec, Canada

Correspondence

Andrea Benedetti, Research Institute of the McGill University Health Centre, 3D.59, 5252 boul de Maisonneuve, Montreal, Quebec H4A 3S5, Canada.

Email: andrea.benedetti@mcgill.ca

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First published: 25 September 2019
Citations: 4

Abstract

We consider the problem of meta‐analyzing two‐group studies that report the median of the outcome. Often, these studies are excluded from meta‐analysis because there are no well‐established statistical methods to pool the difference of medians. To include these studies in meta‐analysis, several authors have recently proposed methods to estimate the sample mean and standard deviation from the median, sample size, and several commonly reported measures of spread. Researchers frequently apply these methods to estimate the difference of means and its variance for each primary study and pool the difference of means using inverse variance weighting. In this work, we develop several methods to directly meta‐analyze the difference of medians. We conduct a simulation study evaluating the performance of the proposed median‐based methods and the competing transformation‐based methods. The simulation results show that the median‐based methods outperform the transformation‐based methods when meta‐analyzing studies that report the median of the outcome, especially when the outcome is skewed. Moreover, we illustrate the various methods on a real‐life data set.

Number of times cited according to CrossRef: 4

  • Real-world effectiveness of nivolumab in patients with non-small-cell lung cancer: a systematic review and meta-analysis, Future Oncology, 10.2217/fon-2020-0248, (2020).
  • Clinical laboratory parameters associated with severe or critical novel coronavirus disease 2019 (COVID-19): A systematic review and meta-analysis, PLOS ONE, 10.1371/journal.pone.0239802, 15, 10, (e0239802), (2020).
  • Systematic review and meta-analysis of the effects of iodine supplementation on thyroid function and child neurodevelopment in mildly-to-moderately iodine-deficient pregnant women, The American Journal of Clinical Nutrition, 10.1093/ajcn/nqaa071, (2020).
  • Donor‐derived cell‐free DNA as a biomarker for rejection after kidney transplantation: a systematic review and meta‐analysis, Transplant International, 10.1111/tri.13753, 0, 0, (2020).

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