Michael & Susan Dell Center for Healthy Living, Division of Health Promotion and Behavioral Sciences, University of Texas School of Public Health, Houston, Texas, United States of America.
PLoS One. 2013 Aug 16;8(8):e71079. doi: 10.1371/journal.pone.0071079. eCollection 2013.
Statistical interactions are a common component of data analysis across a broad range of scientific disciplines. However, the statistical power to detect interactions is often undesirably low. One solution is to elevate the Type 1 error rate so that important interactions are not missed in a low power situation. To date, no study has quantified the effects of this practice on power in a linear regression model.
A Monte Carlo simulation study was performed. A continuous dependent variable was specified, along with three types of interactions: continuous variable by continuous variable; continuous by dichotomous; and dichotomous by dichotomous. For each of the three scenarios, the interaction effect sizes, sample sizes, and Type 1 error rate were varied, resulting in a total of 240 unique simulations.
In general, power to detect the interaction effect was either so low or so high at α = 0.05 that raising the Type 1 error rate only served to increase the probability of including a spurious interaction in the model. A small number of scenarios were identified in which an elevated Type 1 error rate may be justified.
Routinely elevating Type 1 error rate when testing interaction effects is not an advisable practice. Researchers are best served by positing interaction effects a priori and accounting for them when conducting sample size calculations.
统计交互作用是广泛的科学学科数据分析中的常见组成部分。然而，检测交互作用的统计功效往往不理想。一种解决方案是提高第一类错误率，以便在低功效情况下不会错过重要的交互作用。迄今为止，尚无研究在线性回归模型中定量研究这种实践对功效的影响。
进行了蒙特卡罗模拟研究。指定了一个连续的因变量，以及三种类型的交互作用：连续变量与连续变量；连续变量与二分类变量；二分类变量与二分类变量。对于三种情况中的每一种，都改变了交互作用效果大小、样本量和第一类错误率，共产生了 240 个独特的模拟。
一般来说，在 α = 0.05 时，检测交互作用效果的功效要么非常低，要么非常高，因此提高第一类错误率只会增加模型中包含虚假交互作用的概率。确定了一些情况下可能有理由提高第一类错误率。
在测试交互作用时，常规提高第一类错误率不是明智的做法。研究人员最好在预先假设交互作用并在进行样本量计算时考虑它们。
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