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Sampling-based inference for the generalized time-dependent logistic hazard model

Date
2010
Abstract
Lifetimes which satisfy a non-proportional hazard model may arise in several areas, such as, Medicine, Biometrics, Criminology and Industrial Reliability. For these data it is reasonable to presume that the hazard function is time-dependent, thereby accommodating crossing hazards. Such dependency can be modelled directly by introducing a time-dependent term in the model for the hazard function. Accordingly, in this paper we utilize a generalized time-dependent logistic (GTDL) hazard model which can accommodate non-proportional hazards data. A sampling-based inference procedure based on Markov chain Monte Carlo Methods is developed and the methodology is used to investigate survival from advanced lung cancer in a well known dataset.
Supervisor
Description
peer-reviewed
Publisher
Gowas Publishers
Citation
Journal of Statistical Theory and Applications;9(2), pp. 169-184
Collections
Mathematics & Statistics
BIO-SI - Bio-Statistics & Informatics Project
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MacKenzie_2010_sampling.pdf
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Keywords
Bayesian modelling, GTDL, hazard modelling, MCMC
ULRR Identifiers
https://researchrepository.ul.ie/handle/10344/2817
 https://doi.org/10.34961/4465
Funding code
Funding Information
Science Foundation Ireland (SFI)
Sustainable Development Goals
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Type
Article
Rights
https://creativecommons.org/licenses/by-nc-sa/1.0/
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