A Monte Carlo Analysis of Alternative Meta-Analysis Estimators in the Presence of Publication Bias

Feb 3rd, 2012
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This study uses Monte Carlo analysis to investigate the performances of five different metaanalysis (MA) estimators: the Precision Effect Test (PET) estimator, the Precision Effect Estimate with Standard Errors (PEESE) estimator, the Fixed Effects (FE) estimator, the Weighted Least Squares (WLS) estimator, and the Random Effects (RE) estimator. We consider two types of publication bias: publication bias directed against statistically insignificant estimates, and publication bias directed against wrong-signed estimates. Finally, we consider three cases concerning the distribution of the “true effect”: the Fixed Effects case, where there is only estimate per study, and all studies have the same true effect; the Random Effects case, where there is only one estimate per study, and there is 26 heterogeneity in true effects across studies; and the Panel Random Effects case, where studies have multiple estimates, and true effects are heterogeneous both across and within studies.

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