A Bayesian analysis for longitudinal semicontinuous data with an application to an acupuncture clinical trial

Abstract

In many biomedical applications, researchers encounter semicontinuous data where data are either continuous or zero. When the data are collected over time the observations may be correlated. Analysis of this kind of longitudinal semicontinuous data is challenging due to the presence of strong skewness in the data. A flexible class of zero-inflated models in a longitudinal setting is developed. A Bayesian approach is used to analyze longitudinal data from an acupuncture clinical trial, in which the effects of active acupuncture, sham acupuncture and standard medical care is compared on chemotherapy-induced nausea in patients who were treated for advanced breast cancer. A spline model is introduced into the linear predictor of the model to explore the possibility of a nonlinear treatment effect. Possible serial correlation between successive observations is also accounted using the Brownian motion. Thus, the approach taken in this paper provides for a more flexible modeling framework and, with the use of WinBUGS, provides for a computationally simpler approach than direct maximum-likelihood. The Bayesian methodology is illustrated with the acupuncture clinical trial data.