A Multivariate Cure Model for Left- and Right-Censored Data with Application to Colorectal Cancer Screening Patterns

Abstract: We develop a multivariate cure survival model to estimate lifetime patterns of colorectal cancer screening. Screening data cover long periods of time, with sparse observations for each person. Some events may occur before the study begins or after the study ends, so the data are both left- and right-censored, and some individuals are never screened (the "cured" population). We propose a multivariate parametric cure model that can be used with left- and right-censored data. Our model allows for the estimation of the time to screening as well as the average number of times individuals will be screened. We calculate likelihood functions based on the observations for each subject using a distribution that accounts for within-subject correlation, and estimate parameters using Markov Chain Monte Carlo methods. We apply our methods to the estimation of lifetime colorectal cancer screening behavior in the SEER-Medicare data set.