Analyzing preventive trials with generalized additive models.

Abstract

Described a new class of nonparametric regression procedures called generalized additive models (Hastie and Tibshirani, 1991) for assessing intervention effects in mental health preventive field trials. Such models are often better than analysis of covariance models for examining intervention effects adjusted for one or more baseline characteristics and for assessing potential interactions between the intervention and baseline characteristics. Because of these advantages, generalized additive models are important tools analysts should consider in evaluating preventive field trials. We apply generalized additive models as well as more standard linear models to data from a preventive trial aimed at improving mental health and school performance outcomes through a universal intervention in first and second grades. Practical guidance is given to researchers regarding when generalized additive models would be beneficial.