Bayesian Inference for Multivariate Survival Data with a Cure Fraction

Semiparametric Transformation Models for Survival Data with a Cure Fraction

We propose a class of transformation models for survival data with a cure fraction. The class of transformation models is motivated by biological considerations, and it includes both the proportional hazards and the proportional odds cure models as two special cases. An efficient recursive algorithm is proposed to calculate the maximum likelihood estimators. Furthermore, the maximum likelihood ...

Nonparametric Bayesian Inference For Survival Data

This article introduces a new Bayesian approach to the analysis of right-censored survival data. The hazard rate of interest is modeled as a product of conditionally independent stochastic processes, corresponding to (1) a baseline hazard function, and (2) a regression function representing the temporal innuence of the covariates. These processes jump at times that form time-homogeneous Poisson...

Bayesian inference for smoking cessation with a latent cure state.

We present a Bayesian approach to modeling dynamic smoking addiction behavior processes when cure is not directly observed due to censoring. Subject-specific probabilities model the stochastic transitions among three behavioral states: smoking, transient quitting, and permanent quitting (absorbent state). A multivariate normal distribution for random effects is used to account for the potential...

Bayesian Inference for Survival Data with Nonparametric Hazards and Vague

Statistical inference is reviewed for survival data applications with hazard models having one parameter per distinct failure time and using Jeffreys' (1961) vague priors. Distinction between a discrete hazard and a piecewise exponential model is made. Bayes estimators of survival probabilities ace derived. For a single sample and a discrete hazard, the Bayes estimator is shown to be larger tha...

Frailty models with a cure fraction for modeling clustered discrete survival data