Faculty, Staff and Student Publications

Publication Date

4-1-2025

Journal

Statistical Methods in Medical Research

Abstract

With the advancement of medical treatments, many historically incurable diseases have become curable. An accurate estimation of the cure rates is of great interest. When there are no clear biomarker indicators for cure, the estimation of cure rate is intertwined with and influenced by the specification of hazard functions for uncured patients. Consequently, the commonly used proportional hazards (PH) assumption, when violated, may lead to biased cure rate estimation. Meanwhile, longitudinal biomarker measurements for individual patients are usually available. To accommodate non-PH functions and incorporate individual longitudinal biomarker trajectories, we propose a new joint model for cure, survival, and longitudinal data, with hazard ratios between different covariate subgroups flexibly varying over time. The proposed joint model has individual random effects shared between its longitudinal and cure-survival submodels. The regression parameters are estimated by maximization of the non-parametric likelihood via the Monte Carlo expectation-maximization algorithm. The standard error estimation applies a jackknife resampling method. In simulation studies, we consider crossing and non-crossing survival curves, and the proposed model provides unbiased estimates for the cure rates. Our proposed joint cure model is illustrated via a study of chronic myeloid leukemia.

Keywords

Humans, Longitudinal Studies, Proportional Hazards Models, Monte Carlo Method, Survival Analysis, Models, Statistical, Algorithms, Likelihood Functions, Computer Simulation, Biomarkers

DOI

10.1177/09622802251320793

PMID

40017371

PMCID

PMC12075895

PubMedCentral® Posted Date

2-28-2025

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

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