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

TL;DR

This paper introduces a multivariate cure survival model tailored for left- and right-censored data, enabling analysis of lifetime colorectal cancer screening patterns with complex censored observations.

Contribution

It presents a novel multivariate parametric cure model that handles censored data and estimates both timing and frequency of screenings, using MCMC for parameter estimation.

Findings

Successfully applied to SEER-Medicare data

Estimated lifetime screening behaviors

Accounted for within-subject correlation

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.