A Bayesian promotion time cure model with current status data

TL;DR

This paper introduces a Bayesian promotion time cure model for analyzing current status data with a cure fraction, enabling better estimation of non-susceptible individuals and disease dynamics in medical studies.

Contribution

It presents the first Bayesian inference method for current status data with a cure fraction using a promotion time cure model, employing an adaptive Metropolis-Hastings algorithm.

Findings

Simulation studies demonstrate the method's efficiency.

Application to lung tumor and breast cancer data shows practical utility.

Potential to improve clinical cure rate estimation.

Abstract

Analysis of lifetime data from epidemiological studies or destructive testing often involves current status censoring, wherein individuals are examined only once and their event status is recorded only at that specific time point. In practice, some of these individuals may never experience the event of interest, leading to current status data with a cured fraction. Cure models are used to estimate the proportion of non-susceptible individuals, the distribution of susceptible ones, and covariate effects. Motivated from a biological interpretation of cancer metastasis, promotion time cure model is a popular alternative to the mixture cure rate model for analysing such data. The current study is the first to put forth a Bayesian inference procedure for analysing current status data with a cure fraction, resorting to a promotion time cure model. An adaptive Metropolis-Hastings algorithm is utilised for posterior computation. Simulation studies prove our approach's efficiency, while analyses of lung tumor and breast cancer data illustrate its practical utility. This approach has the potential to improve clinical cure rates through the incorporation of prior knowledge regarding the disease dynamics and therapeutic options.