Tofighi Davood
University of New Mexico, Albuquerque, NM, United States.
Front Psychol. 2020 Jan 20;10:2989. doi: 10.3389/fpsyg.2019.02989. eCollection 2019.
In mediation analysis, conditions necessary for commonly recommended tests, including the confidence interval (CI)-based tests, to produce an accurate Type I error, do not generally hold for finite sample sizes and non-normally distributed model residuals. This is typically the case because of the complexity of testing a null hypothesis about indirect effects. To remedy these issues, we propose two extensions of the recently developed asymptotic Model-based Constrained Optimization (MBCO) likelihood ratio test (LRT), a promising new model comparison method for testing a general function of indirect effects. The proposed tests, semi-parametric and parametric bootstrap MBCO LRT are shown to yield a more accurate Type I error rate in smaller sample sizes and under various degrees of non-normality of the model residuals compared to the asymptotic MBCO LRT and the CI-based methods. We provide R script in the Supplemental Materials to perform all three MBCO LRTs.
在中介分析中，对于常用推荐检验（包括基于置信区间（CI）的检验）而言，产生准确的I型错误所需的条件，通常在有限样本量和非正态分布的模型残差情况下并不成立。这通常是由于检验关于间接效应的零假设的复杂性所致。为解决这些问题，我们提出了最近开发的基于渐近模型的约束优化（MBCO）似然比检验（LRT）的两种扩展方法，这是一种用于检验间接效应的一般函数的有前景的新模型比较方法。与渐近MBCO LRT和基于CI的方法相比，所提出的半参数和参数自助法MBCO LRT在较小样本量以及模型残差的各种非正态程度下，都能产生更准确的I型错误率。我们在补充材料中提供了R脚本以执行所有三种MBCO LRT。
