Detecting and correcting publication bias in meta-analysis

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Date

2009-12

Authors

Li, Xin

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Abstract

Publication bias (PB) makes the resources for meta-analysis (M-A) unreliable in the sense of completion and accuracy, so to investigate, identify and correct PB is a very important issue in M-A. The current study proposed an empirical comparison in both detection and correcting PB, using a Monte Carlo study. Conditions to be manipulated include the number of primary studies, number of missing studies and true effect size. RANNOR in SAS will be used to generate normally distributed random variables and, for each condition, 10,000 M-As will be simulated. Type I error rates are to be calculated for the conditions with no PB and powers were estimated for the conditions with PB and adequate type I error control. Finally, a demonstration of how M-A can and should be used as a part of program evaluations was given.

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