Statistical Modeling of Data from Smoking Cessation Clinical Trials

In smoking cessation clinical trials, subjects commonly experience a series of lapse and recovery episodes of varying lengths. Any quit episode may become permanent, in the sense that the subject stops smoking for good, and any lapse may also become permanent, in the sense that the subject abandons the quit attempt entirely. Individual quit patterns may reflect the effects of treatment and measured and unmeasured covariates. To describe this complex data structure, we propose a multivariate time-to-event model that i) incorporates alternating recurrent events of two types, each with the possibility of "cure", ii) allows for the modifying effects of treatment and covariates, and iii) reflects within-subject correlation via frailties. Specifically, we introduce a novel cure-mixture frailty model in which the cure probability follows a binary regression and the time to event given not cured is determined by a proportional hazard model. We then extend it to data with recurring events of two alternating types, where we assume that each type of event has a gamma frailty, and we link the frailties by means of a Clayton copula. In my first project, I fit this model to data from a smoking cessation drug trial. In my second project, I developed a Bayesian method to predict individual long-term smoking behavior from observed short-term quit/relapse patterns. In my third project, I investigated the theoretical properties of the survival distribution, evidently not previously described, that arises from our cure-mixture frailty model. Degree Type Dissertation Degree Name Doctor of Philosophy (PhD) Graduate Group Epidemiology & Biostatistics First Advisor Daniel F Heitjan