Testing for Homogeneity in Meta-Analysis I. The One Parameter Case: Standardized Mean Difference

Details Testing for Homogeneity in Meta-Analysis I. The One Parameter Case: Standardized Mean Difference Testing for Homogeneity in Meta-Analysis I. The One Parameter Case: Standardized Mean Difference

2009-06-16

arxiv.org/abs/0906.2999v2

Statistics

Methodology

42 pages, 6 figures. v2: major revision. Added simulations for a chi-square approximation based on the estimated mean of Q. Th

Scientific paper

Meta-analysis seeks to combine the results of several experiments in order to improve the accuracy of decisions. It is common to use a test for homogeneity to determine if the results of the several experiments are sufficiently similar to warrant their combination into an overall result. Cochran's Q statistic is frequently used for this homogeneity test. It is often assumed that Q follows a chi-square distribution under the null hypothesis of homogeneity, but it has long been known that this asymptotic distribution for Q is not accurate for moderate sample sizes. Here we present formulas for the mean and variance of Q under the null hypothesis which represent O(1/n) corrections to the corresponding chi-square moments in the one parameter case. The formulas are fairly complicated, and so we provide a program (available at http://www.imperial.ac.uk/stathelp/researchprojects/metaanalysis) for making the necessary calculations. We apply the results to the standardized mean difference (Cohen's d-statistic) and consider two approximations: a gamma distribution with estimated shape and scale parameters and the chi-square distribution with fractional degrees of freedom equal to the estimated mean of Q. We recommend the latter distribution as an approximate distribution for Q to use for testing the null hypothesis.

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