Bayesian Regression Models for the Quality Adjusted Lifetime Data with Zero Time Duration Health States

Abstract

Clinical trial studies are often conducted in which quality of life is accessed and recorded along with other clinically measurable endpoints. Consideration of the quality of life in addition to the survival time in the statistical analysis can result in a better assessment of the treatments being compared. Quality adjusted lifetime (QAL) data analysis can serve as an important tool to the medical and patient community. This article presents a Bayesian regression approach to the modeling of censored QAL data. The Bayesian hierarchical framework based on a progressive health state model with a data augmentation scheme which provides a nonzero probability to the zero time spent in any health state has been developed. Simulation studies using Markov Chain Monte Carlo (MCMC) methods were performed to validate the proposed method. A real data set was used to illustrate the application of the proposed method.