Abstract:
:We propose a hierarchical Bayesian methodology to model spatially or spatio-temporal clustered survival data with possibility of cure. A flexible continuous transformation class of survival curves indexed by a single parameter is used. This transformation model is a larger class of models containing two special cases of the well-known existing models: the proportional hazard and the proportional odds models. The survival curve is modeled as a function of a baseline cumulative distribution function, cure rates, and spatio-temporal frailties. The cure rates are modeled through a covariate link specification and the spatial frailties are specified using a conditionally autoregressive model with time-varying parameters resulting in a spatio-temporal formulation. The likelihood function is formulated assuming that the single parameter controlling the transformation is unknown and full conditional distributions are derived. A model with a non-parametric baseline cumulative distribution function is implemented and a Markov chain Monte Carlo algorithm is specified to obtain the usual posterior estimates, smoothed by regional level maps of spatio-temporal frailties and cure rates. Finally, we apply our methodology to melanoma cancer survival times for patients diagnosed in the state of New Jersey between 2000 and 2007, and with follow-up time until 2007.
journal_name
Stat Methods Med Resjournal_title
Statistical methods in medical researchauthors
Hurtado Rúa SM,Dey DKdoi
10.1177/0962280212445658subject
Has Abstractpub_date
2016-02-01 00:00:00pages
167-87issue
1eissn
0962-2802issn
1477-0334pii
0962280212445658journal_volume
25pub_type
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