Estimating effect size under publication bias: Small sample properties and robustness of a random effects selection model
Estimating effect size under publication bias: Small sample properties and robustness of a random effects selection model
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DOI：
10.2307/1165338
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发表时间：
1996-12-01
影响因子：
2.4
通讯作者：
Vevea, JL
中科院分区：
文献类型：
--
作者：
Hedges, LV;Vevea, JL
当存在发表偏倚时，产生大p值的研究，因此小的效应估计，不太可能被发表，这导致荟萃分析中效应的偏倚估计。我们研究了随机效应模型中基于单轨p值的选择模型。该程序既模拟了选择过程，又校正了选择对效应参数均值和方差估计的影响。还提供了选择的统计显著性检验。的小样本特性，该方法通过模拟进行评估，渐近理论被认为是合理准确的正确的模型规格和合理的条件下。该方法大大减少了偏差，由于选择时，模型规格是正确的，但估计的方差增加，因此，只有当选择产生实质性的偏差，均方误差减少。还通过模拟研究了该方法对违反随机效应分布形式假设的鲁棒性，并且通常发现平均效应的模型校正估计值比未校正估计值的偏倚要小得多。选择偏差的显著性检验，但是，被发现是高度非鲁棒性，拒绝高达10倍的名义率时，没有选择，但分布的影响是不正确的指定。
When there is publication bias, studies yielding large p values, and hence small effect estimates, are less likely to be published, which leads to biased estimates of effects in meta-analysis. We investigate a selection model based on one-railed p values in the context of a random effects model. The procedure both models the selection process and corrects for the consequences of selection on estimates of the mean and variance of effect parameters. A test of the statistical significance of selection is also provided. The small sample properties,of the method are evaluated by means of simulations, and the asymptotic theory is found to be reasonably accurate under correct model specification and plausible conditions. The method substantially reduces bias due to selection when model specification is correct, but the variance of estimates is increased; thus mean squared error is reduced only when selection produces substantial bias. The robustness of the method to violations of assumptions about the form of the distribution of the random effects is also investigated via simulation, and the model-corrected estimates of the mean effect are generally found to be much less biased than the uncorrected estimates. The significance test for selection bias, however; is found to be highly nonrobust, rejecting at up to 10 times the nominal rate when there is no selection but the distribution of the effects is incorrectly specified.