Performance of location-scale models in meta-analysis: A simulation study.

IF 4.6 2区 心理学 Q1 PSYCHOLOGY, EXPERIMENTAL
Desirée Blázquez-Rincón, José Antonio López-López, Wolfgang Viechtbauer
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引用次数: 0

Abstract

Location-scale models in the field of meta-analysis allow researchers to simultaneously study the influence of moderator variables on the mean (location) and variance (scale) of the distribution of true effects. However, the increased complexity of such models can make model fitting challenging. Moreover, the statistical properties of the estimation and inference methods for such models have not been systematically examined in the meta-analytic context. We therefore conducted a Monte Carlo simulation study to compare different estimation methods (maximum or restricted maximum likelihood estimation), significance tests (Wald-type, permutation, and likelihood-ratio tests), and methods for constructing confidence intervals (Wald-type and profile-likelihood intervals) for the scale coefficients of such models. When restricted maximum likelihood estimation was used, slightly closer to nominal rejection rates and narrower confidence intervals were obtained. The permutation test yielded type I error rates closest to the nominal level, whereas the likelihood-ratio test obtained the highest statistical power. In most scenarios, profile-likelihood intervals showed lower coverage probabilities than the Wald-type method but closer to the nominal 95% level. Finally, slightly higher rejection rates and coverage probabilities were obtained when a dichotomous moderator was examined rather than a continuous one. Despite the need to use some constraints on the parameter space for the scale coefficients and the possibility of non-convergence of some procedures that may affect the fitting of the specified models, location-scale models proved to be a valid and useful tool for modeling the heterogeneity parameter in meta-analysis.

位置尺度模型在meta分析中的表现:一项模拟研究。
meta分析领域的位置尺度模型允许研究者同时研究调节变量对真实效应分布的均值(位置)和方差(尺度)的影响。然而,这些模型的复杂性增加会使模型拟合具有挑战性。此外,在元分析的背景下,这些模型的估计和推理方法的统计特性还没有得到系统的检验。因此,我们进行了蒙特卡罗模拟研究,以比较不同的估计方法(最大或限制最大似然估计)、显著性检验(wald型、排列和似然比检验)以及为这些模型的尺度系数构建置信区间的方法(wald型和轮廓似然区间)。当使用限制最大似然估计时,得到的结果略接近于名义拒绝率和较窄的置信区间。排列检验产生的I型错误率最接近名义水平,而似然比检验获得了最高的统计功率。在大多数情况下,轮廓似然区间显示的覆盖概率低于wald型方法,但更接近名义上的95%水平。最后,当检查二分类调节因子时,获得略高于连续调节因子的拒绝率和覆盖概率。尽管需要对尺度系数的参数空间使用一些约束,并且某些过程可能不收敛,这可能会影响指定模型的拟合,但位置尺度模型被证明是在meta分析中建模异质性参数的有效和有用的工具。
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来源期刊
CiteScore
10.30
自引率
9.30%
发文量
266
期刊介绍: Behavior Research Methods publishes articles concerned with the methods, techniques, and instrumentation of research in experimental psychology. The journal focuses particularly on the use of computer technology in psychological research. An annual special issue is devoted to this field.
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