Microarray experiments are a typical example of small sample size designs. Hedges and Olkin ( 1985) and Stangl and Berry ( 2000) provide good reviews of meta-analysis techniques. Meta-analysis, which consists in combining data or results from different studies, has been widely used in medicine and health policy to interpret contradictory results from various studies or overcome the problem of reduced statistical power in studies with small sample sizes. Although the proposed moderated effect size combination improved already existing effect size approaches, the P-value combination was found to provide a better sensitivity and a better gene ranking than the other meta-analysis methods, while effect size methods were more conservative.Īvailability: An R package metaMA is available on the CRAN. All methods were applied to real publicly available datasets on prostate cancer, and were compared in an extensive simulation study for various amounts of inter-study variability. Results: A moderated effect size combination method was proposed and compared with other meta-analysis approaches. In microarray experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably increase the statistical power and give more accurate results. Motivation: With the proliferation of microarray experiments and their availability in the public domain, the use of meta-analysis methods to combine results from different studies increases.
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