| Estimation of the extra cost that is required to improve the efficacy of a treatment is an important problem of contemporary medical research. As a result, many clinical trials now collect cost information as well as effectiveness data. We propose parametric Bayesian approaches to compare the cost-effectiveness of a new treatment to an existing control when the outcomes are measured longitudinally and the data are subject to censoring. We first use a pattern-mixture model to describe the joint distribution of the cost and survival. The model assumes that cost, conditional on survival, follows a multivariate normal distribution. We simulate the posterior distribution via data augmentation and apply the method to data from a randomized clinical trial of a treatment for a cardiovascular disease. We use two sets of priors, noninformative priors and subjective priors. We compare findings from our approach with an analysis using Willan and Lin's frequentist nonparametric method.; Quality-of-life (QoL) is also important in evaluating the effect of a treatment on patients. Recently, the number of trials that compare patient QoL as a secondary endpoint has increased. To get a more complete comparison of the treatment and control, we expand the above method, so that the cost-effectiveness analysis adjusts for QoL measures. The pattern-mixture model assumes that cost and QoL, conditional on survival, follow a multivariate normal distribution. Simulation of the posterior distribution and prior density selections are similar to the unadjusted cost-effectiveness method. We again compare the results from our pattern-mixture models with Willan et al.'s frequentist nonparametric method.; Finally, we propose a frailty model that allows us to jointly model the survival, cost and QoL data with a small number of parameters and describe the within subject correlation in an intuitively appealing way. The model assumes that the survival, logic-transformed QoL and cost at each time point, given the frailty, follow exponential, normal, and gamma distributions, respectively. We use subjective priors and simulate the posterior distribution via importance sampling. We apply this method to the cardiovascular clinical trial data. |
