元回归中效应量的最小个数需求：基于统计功效和估计精度
Junyan Fang, Zhang Minqiang
Abstract
<p id="C2">Meta-regression is the most frequently used technique for identifying moderators in meta-analysis. In this study, main principles and basic models of meta-analysis and meta-regression were briefly introduced first. Then a Monte Carlo simulation was conducted to investigate the minimum number of the effect size required in meta-regression based on statistical power and estimation precision. The results showed that (1) the Wald-type <italic>z</italic> test was prone to type I error in meta-regression; (2) at least 20 effect sizes were needed to meet parameter estimation requirements; (3) and inclusion of proper moderators could reduce the number of effect size required. Therefore, it is suggested that (1) meta-analysts should be careful when using the CMA software and the Wald-type <italic>z</italic> test; (2) at least 20 or more effect sizes are generally needed based on different situations; (3) exploration of moderators is necessary; (4) reviewers can value a meta-analysis research according to the minimum number of effect size required.