Benefit-risk assessment is a crucial step in the medical decision process. In many biomedical studies, both longitudinal marker measurements and time to a terminal event serve as important endpoints for benefit-risk assessment. The effect of an intervention or a treatment on the longitudinal marker process, however, can be in conflict with its effect on the time to the terminal event. Thus questions arise on how to evaluate treatment effects based on the two endpoints, for the purpose of deciding on which treatment is most likely to benefit the patients. In this dissertation, we present a unified framework for benefit-risk assessment using the observed longitudinal markers and time to event data. We propose a cumulative weighted marker process to synthesize information from the two endpoints, and use its mean function at a pre-specified time point as a benefit-risk summary measure. We consider nonparametric estimation of the summary measure under two scenarios: (i) the longitudinal marker is measured intermittently during the study period, and (ii) the value of the longitudinal marker is observed throughout the entire follow-up period. The large-sample properties of the estimators are derived and compared. Simulation studies and the application to an AIDS clinical trial exhibit that the proposed methods are easy to implement and reliable for practical use.
In many follow-up or surveillance studies, marker data are collected conditioning on the occurrence of recurrent events. In contrast with the above situation that the marker measurements exists at any time before the terminal event, sometimes marker measurements are triggered by the occurrence of recurrent events. Examples include the medical cost for inpatient or outpatient cares, length-of-stay for hospitalizations, and prognostic or quality-of-life measurement repeatedly measured at multiple infections related to a certain disease. A recurrent marker process, defined between a pre-specified time origin and a terminal event, is composed of recurrent events and repeatedly measured marker measurements. We consider nonparametric estimation of the mean recurrent marker process in the situation when the occurrence of terminal event is subject to competing risks. Statistical methods and inference are developed to address a variety of questions and applications, for the purposes of estimating and comparing the integrated risk in relation to recurrent events, marker measurements and time to the terminal event for different competing risk groups. A SEER-Medicare linked database is used to illustrate the proposed approaches
