Assessing program effects in the presence of treatment-baseline interactions: a latent curve approach.
Methods for assessing treatment effects of longitudinal randomized intervention are considered. The focus is on modeling the nonlinear relationship between treatment effects and baseline often observed in prevention programs designed for at-risk populations. Piecewise linear growth modeling was used to study treatment effects during the different periods of development. A multistep multiple-group analysis procedure is proposed for assessing treatment effects in the presence of nonlinear treatment-baseline interactions. Standard errors of the estimates from this multistep procedure were obtained using a bootstrap approach. The methods are illustrated using data from the Johns Hopkins Prevention Research Center involving an intervention aimed at improving classroom behavior.
Khoo
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Department of Psychology, Arizona State University, Main Campus, P.O. Box 871104, Tempe, Arizona 85287-1104, USA. stkhoo@asu.edu
D006801:Humans; D008960:Models, Psychological; D011897:Random Allocation