A class of parametric dynamic survival models

A class of parametric dynamic survival models are explored in which only limited parametric assumptions are made, whilst avoiding the assumption of proportional hazards. Both the log-baseline hazard and covariate effects are modelled by piecewise constant and correlated processes. The method of esti...

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Bibliographic Details
Published in:Lifetime data analysis 2005-03, Vol.11 (1), p.81-98
Main Authors: Hemming, K, Shaw, J E H
Format: Article
Language:English
Subjects:
Datasets
Estimates
Female
Frailty
Humans
Life Tables
Likelihood Functions
Male
Markov analysis
Nephritis - mortality
Normal distribution
Proportional Hazards Models
Random Allocation
Sensitivity and Specificity
Stomach Neoplasms - mortality
Survival Analysis
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Summary:A class of parametric dynamic survival models are explored in which only limited parametric assumptions are made, whilst avoiding the assumption of proportional hazards. Both the log-baseline hazard and covariate effects are modelled by piecewise constant and correlated processes. The method of estimation is to use Markov chain Monte Carlo simulations: Gibbs sampling with a Metropolis-Hastings step. In addition to standard right censored data sets, extensions to accommodate interval censoring and random effects are included. The model is applied to two well known and illustrative data sets, and the dynamic variability of covariate effects investigated.
ISSN:1380-7870
1572-9249
DOI:10.1007/s10985-004-5641-5