Assessing Baseline and Post-Baseline Effects in Repeated Measures Data Analysis: An Example Using Smoking Cessation Data
Zhou, X., Sherrill, B. H., & Wang, J. (2007, August). Assessing Baseline and Post-Baseline Effects in Repeated Measures Data Analysis: An Example Using Smoking Cessation Data. Presented at ASA JSM 2007, Salt Lake City, UT.
In behavior modification studies dynamically changing factors have complex effects on subsequent actions. The question is how to accommodate baseline and post-baseline factors in one flexible model and assess effects on outcomes that are of clinical relevance. For example, in a longitudinal study about smoking relapse, potential predictors for successful smoking cessation include: nicotine dependence, mood disturbance, and weight change. Several models could be fit to the data depending on the clinical question of interest; often in exploratory analyses, however, several models are fit to the data. This presents a particular challenge to the analyst if competing inferences are made. We developed a single flexible model that can be used to test a variety of null hypotheses, thus providing the analyst a more efficient tool for evaluating complex time-varying covariates.