Meta‐analysis of the difference of medians

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 auth...

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Vydáno v:Biometrical journal Ročník 62; číslo 1; s. 69 - 98
Hlavní autoři: McGrath, Sean, Sohn, Hojoon, Steele, Russell, Benedetti, Andrea
Médium: Journal Article
Jazyk:angličtina
Vydáno: Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.01.2020
Témata:
Analysis of Variance
Biometry - methods
Genetic transformation
median
Median (statistics)
Meta-analysis
Meta-Analysis as Topic
Models, Statistical
quantile estimation
Sampling methods
skewed data
Statistical methods
Transformations
two‐group
Variance
meta-analysis
quantile estimation
two-group
median
skewed data
ISSN:0323-3847, 1521-4036, 1521-4036
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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.
AbstractList 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.
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.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.
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.
Author Benedetti, Andrea
Sohn, Hojoon
McGrath, Sean
Steele, Russell
Author_xml – sequence: 1
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  orcidid: 0000-0001-5837-3844
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  surname: Steele
  fullname: Steele, Russell
  organization: McGill University
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  givenname: Andrea
  orcidid: 0000-0002-8314-9497
  surname: Benedetti
  fullname: Benedetti, Andrea
  email: andrea.benedetti@mcgill.ca
  organization: McGill University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/31553488$$D View this record in MEDLINE/PubMed
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Keywords meta-analysis
quantile estimation
two-group
median
skewed data
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Snippet 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...
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...
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StartPage 69
SubjectTerms Analysis of Variance
Biometry - methods
Genetic transformation
median
Median (statistics)
Meta-analysis
Meta-Analysis as Topic
Models, Statistical
quantile estimation
Sampling methods
skewed data
Statistical methods
Transformations
two‐group
Variance
Title Meta‐analysis of the difference of medians
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fbimj.201900036
https://www.ncbi.nlm.nih.gov/pubmed/31553488
https://www.proquest.com/docview/2331715764
https://www.proquest.com/docview/2297128158
Volume 62
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