A non-proportional hazards cure model with an application to gastric cancer data analysis

In many practical situations, some subjects may never experience the event of interest in their lifetime. These subjects are referred to as the cured or non-susceptible subjects. In the context of chronic disease treatment, this is referred to as a cure fraction. In this work, we extend the generalized time-dependent logistic (GTDL) model proposed by MacKenzie (1996) to a flexible family of models which accommodates not only non-proportional hazards, but also long-term survivors. Inferential methods are then developed for the proposed model and a Monte Carlo simulation study is also carried out to evaluate the performance of the model as well as the inferential method developed here. A real data example on gastric cancer is then used to illustrate the usefulness of the proposed model.

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Publication Details

Journal
Statistical Methods in Medical Research
Published
2026-01-30
DOI
https://doi.org/10.1177/09622802251414429
Primary Topic
Statistical Methods and Inference
Type
article
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article

A non-proportional hazards cure model with an application to gastric cancer data analysis

M Mar Fenoy, N Balakrishnan, M Carmen Pardo
Statistical Methods in Medical Research
Statistical Methods and Inference
article

A non-proportional hazards cure model with an application to gastric cancer data analysis

M Mar Fenoy, N Balakrishnan, M Carmen Pardo
article en

Abstract

In many practical situations, some subjects may never experience the event of interest in their lifetime. These subjects are referred to as the cured or non-susceptible subjects. In the context of chronic disease treatment, this is referred to as a cure fraction. In this work, we extend the generalized time-dependent logistic (GTDL) model proposed by MacKenzie (1996) to a flexible family of models which accommodates not only non-proportional hazards, but also long-term survivors. Inferential methods are then developed for the proposed model and a Monte Carlo simulation study is also carried out to evaluate the performance of the model as well as the inferential method developed here. A real data example on gastric cancer is then used to illustrate the usefulness of the proposed model.

Statistical Methods in Medical Research
Universidad Complutense de Madrid (ES), McMaster University (CA)
Good health and well-being
Openalex Percentile: Top 75%
Statistical Methods and Inference
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