Change-point cure models with application to estimating the change-point effect of age of diagnosis among prostate cancer patients

Abstract

Previous research on prostate cancer survival trends in the United States National Cancer Institute’s Surveillance Epidemiology and End Results database has indicated a potential change-point in the age of diagnosis of prostate cancer around age 50. Identifying a change-point value in prostate cancer survival and cure could have important policy and health care management implications. Statistical analysis of this data has to address two complicating features: (1) change-point models are not smooth functions and so present computational and theoretical difficulties; and (2) models for prostate cancer survival need to account for the fact that many men diagnosed with prostate cancer can be effectively cured of their disease with early treatment. We develop a cure survival model that allows for change-point effects in covariates to investigate a potential change-point in the age of diagnosis of prostate cancer. Our results do not indicate that age under 50 is associated with increased hazard of death from prostate cancer.

Determination of a Change Point in the Age at Diagnosis of Breast Cancer Using a Survival Model

“…If cure ratio estimation is close to zero, then the cure model might not be appropriate (Othus et al, 2012). Censoring data have random patterns.…”

“…We will reach change point of breast cancer diagnosis age and cure ratio for the breast cancer patients in Tehran using mixture cure model with exponential distribution which has one change point in age covariate (Othus et al, 2012). It is noteworthy that likelihood function falls into two sections in this model: the first section of likelihood function is related to data which are smaller than change point parameter; the second section is related to larger data than change point parameter.…”

Determination of Cut Point in the Age of Colorectal Cancer Diagnosis Using a Survival Cure Model

“…Survival cure models are utilized to analyze data when there is a chance of survival long time and cured (Machin et al, 2006;Taweab et al, 2015). Survival cure model is utilized in this study to determine cut point in CRC diagnosis age so that exponential mixture cure model with one cut point in age covariate is utilized (Othus et al, 2012). We obtained cut point in CRC diagnosis age and cure rate in CRC patients in Tehran, Iran.…”

“…In change point exponential cure model, likelihood function is divided into two sections: less than and greater than the change point parameter (Othus et al, 2012). In each section, exponential distribution was used.…”

Bayesian random threshold estimation in a Cox proportional hazards cure model

“…Othus et al . estimated a threshold in a covariate in the cure model setting. They assume that there is a threshold in a covariate z 2 , where a sudden jump or fall occurs in the hazard value or cure probability.…”