Bayesian defective Marshall-Olkin Gompertz model: an integrated approach to identifying cure fraction

TL;DR

This paper introduces a Bayesian defective regression model based on the Marshall-Olkin Gompertz distribution for analyzing long-term survival data with cure fractions, demonstrated through a real-world cancer study.

Contribution

It presents a novel Bayesian defective regression model for survival analysis that effectively captures cure fractions using the Marshall-Olkin Gompertz distribution.

Findings

Identification of long-term survivors in testicular cancer data

Estimation of cure probabilities with credible intervals

Assessment of risk factors like age, treatment, and cancer stage

Abstract

Regression models have a substantial impact on interpretation of treatments, genetic characteristics and other potential risk factors in survival analysis. In many applications, the description of censoring and survival curve reveals the presence of cure fraction on data, which leads to alternative modeling. The most common approach to introduce covariates under a parameter estimation is the cure rate model and its variations, although the use of defective distributions have introduced a more parsimonious and integrated approach. Defective distributions are given by a density function whose integration is not one after changing the domain of one of the parameters, making them appropriate for survival curves with an evident plateau. In this work, we introduce a new Bayesian defective regression model for long-term survival outcomes using the Marshall-Olkin Gompertz distribution. The estimation process is under the Bayesian paradigm. We evaluate the asymptotic properties of our proposal under the vague prior scheme in Monte Carlo studies. We present a motivating real-world application using data from patients diagnosed with testicular cancer in S\~ao Paulo, Brazil, in which long-term survivors were identified. Scenarios of cure with uncertainty estimates via credible intervals are provided to evaluate characteristics such as risk age, presence of treatment, and cancer stage.