In many clinical trials, the quality of life assessments are made at regular intervals together with clinically measurable endpoints. Quality adjusted lifetime (QAL) data analysis can serve as an important tool to the medical and the patient community. This article presents a Bayesian approach to analyze quality adjusted lifetime data. Considering a progressive health state model, a flexible class of parametric mixture distributions is proposed for the log of the time spent in different health states. Viewing the censored observations as missing variables, a data augmentation scheme integrated within a Markov Chain Monte Carlo method to obtain statistical inference. The proposed models are allowed to include several explanatory variables within a regression model. Simulation studies and an analysis of real-life data from a clinical trial, are presented to illustrate the proposed methods.
AMS Subject Classification
62N01 and 62F15
Bayesian Inference Data Augmentation Gibbs Sampling Markov Chain Monte Carlo Quality Adjusted Survival TWiST
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