Modeling of long-term survival data with unobserved dispersion via neural network

Led Red Teh (), Vicente Garibay Cancho () and Josemar Rodrigues ()
Additional contact information
Led Red Teh: University of São Paulo
Vicente Garibay Cancho: University of São Paulo
Josemar Rodrigues: University of São Paulo

Computational Statistics, 2025, vol. 40, issue 8, No 3, 4115-4137

Abstract: Abstract Traditional models in survival analysis assume that every subject will eventually experience the event of interest in the study, such as death or disease recurrence; thus, the survival function is said to be proper. The cure rate model, which was first proposed seven decades ago, accounts for the presence of a fraction of individuals who will never experience the occurrence of the event of interest, referred to as the cure fraction. This cure fraction can be conceptualized as immune or cured subjects in the context of cancer treatment. In the literature, various cure rate models have been widely studied and commonly applied to structured data with a limited number of covariates. Recently, the use of convolutional neural networks, a powerful deep learning technique for image processing, has become increasingly common in the medical field. Medical images, such as histological slides and magnetic resonance images, are directly related to a patient’s prognostic factors. Therefore, it is reasonable to introduce these images as predictors in cure models. In this work, we extend the article by Xie and Yu (Stat Med 40(15):3516–3532, 2021. https://doi.org/10.1002/sim.8980 ), which employed a neural network to model the effect of unstructured predictors in the promotion time cure model setting for cases involving overdispersed data. We refer to our extension as the integrated negative binomial cure rate model, with its parameters estimated through the Expectation–Maximization algorithm.

Keywords: Convolutional neural network; Cure rate model; OASIS-3 Alzheimer’s disease dataset; Survival analysis (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00180-025-01608-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:compst:v:40:y:2025:i:8:d:10.1007_s00180-025-01608-3

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/180/PS2

DOI: 10.1007/s00180-025-01608-3

Access Statistics for this article

Computational Statistics is currently edited by Wataru Sakamoto, Ricardo Cao and Jürgen Symanzik

More articles in Computational Statistics from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().