title: Does raising type 1 error rate improve power to detect interactions in linear regression models? A simulation study.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors: 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.
authors: Casey P
authors: CP
authors: Durand
mesh_terms: D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D009010:Monte Carlo Method; D011336:Probability; D012107:Research Design / Q000706:statistics & numerical data*; D018401:Sample Size
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