  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.
