B0860
Title: Use of multistate model for multiple endpoints in oncology clinical trials analysis and designs
Authors: Chen Hu - Johns Hopkins University (United States) [presenting]
Abstract: In oncology clinical trials, disease progressions are most commonly captured through a series of sequentially observed events, such as cancer recurrence and deaths. The relationship between covariate (e.g., therapeutic intervention), recurrence, and death is often of interest, as it may provide key insights of optimal treatment decisions and future study designs. However such investigation is often complicated by the latency of disease progression leading to undetected or missing progression-related events. We consider a progressive multistate model with a frailty modeling the association between progression and death, and propose a semiparametric regression model for the joint distribution. An Expectation Maximization (EM) approach is used to derive the maximum likelihood estimators of covariate effects on both endpoints, the probability of missing progression event, as well as the parameters involved in the association. The asymptotic properties of the estimators are studied using theory of martingale and empirical process. We evaluate the utility of the proposed model for data analysis and study design based on both Monte Carlo simulations and real data examples.
