errors (false positives and false negatives, respectively). Alternately, Maenner et al. [2009] initially used a machine learning approach, which is not based on p-values so a Bonferroni correction is not applicable. Machine learning approaches can screen large amounts of data and take into account interaction effects as well as main effects without requiring model specification. They then selected a very small number of variables with the highest variable importance scores and tested these for interactions using traditional regression approaches.
