Statistics and Its Interface

Volume 17 (2024)

Number 4

Modeling and identifiability of non-homogenous Poisson process cure rate model

Pages: 733 – 747

DOI: https://dx.doi.org/10.4310/22-SII763

Authors

Soorya Surendren (Cochin University Of Science & Technology)

Asha Gopalakrishnan (Cochin University Of Science & Technology)

Anup Dewanji (Indian Statistical Institute, Kolkatta)

Abstract

The promotion time cure models or bounded cumulative hazards model (BCH) was proposed as an alternative to the mixture cure models. In the present paper, this model is modified to provide a class of cure rate models based on a non-homogeneous Poisson process (NHPP). The properties of this class are studied. Also, when censored observations are present, distinguishing censored individuals from the cured group lead to identifiability issues in the members of this class. These identifiability issues are investigated and finally few members of this class are provided. Simulation results using an example of the NHPP cure rate model with exponentiated intensity and exponential baseline is supplemented. The application of the model is illustrated using E1684 real data from a study that included 284 patients from the Eastern Cooperative Oncology Group (ECOG) phase III clinical trial.

Keywords

cure rate, identifiability, non-homogenous Poisson process

2010 Mathematics Subject Classification

Primary 62N01, 62P10. Secondary 62N02.

Received 21 April 2022

Accepted 4 November 2022

Published 19 July 2024