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Stat Med. 2019 Sep 20;38(21):4013-4025. doi: 10.1002/sim.8278. Epub 2019 Jun 17.

A parametric meta-analysis.

Author information

1
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee.
2
Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut.

Abstract

In a meta-analysis, we assemble a sample of independent, nonidentically distributed p-values. The Fisher's combination procedure provides a chi-squared test of whether the p-values were sampled from the null uniform distribution. After rejecting the null uniform hypothesis, we are faced with the problem of how to combine the assembled p-values. We first derive a distribution for the p-values. The distribution is parameterized by the standardized mean difference (SMD) and the sample size. It includes the uniform as a special case. The maximum likelihood estimate (MLE) of the SMD can then be obtained from the independent, nonidentically distributed p-values. The MLE can be interpreted as a weighted average of the study-specific estimate of the effect size with a shrinkage. The method is broadly applicable to p-values obtained in the maximum likelihood framework. Simulation studies show that our method can effectively estimate the effect size with as few as 6 p-values in the meta-analyses. We also present a Bayes estimator for SMD and a method to account for publication bias. We demonstrate our methods on several meta-analyses that assess the potential benefits of citicoline for patients with memory disorders or patients recovering from ischemic stroke.

KEYWORDS:

Bayesian estimator; citicoline; distribution of p-values; standardized mean difference

PMID:
31206759
PMCID:
PMC6688941
[Available on 2020-09-20]
DOI:
10.1002/sim.8278

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