2024/11/26 by Dionisio Alves da Silva Neto, Neto, Dionisio Alves da Silva, Vera Tomazella +3
Engineering · #Applications (stat.AP) #FOS: Computer and information sciences #Manufacturing Process and Optimization #Metal Forming Simulation Techniques #Metallurgy and Material Forming #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2411.17841
openalex publication_date 2024/11/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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 São 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.