Incorporating patients' characteristics in cost-effectiveness studies with clinical trial data: A flexible Bayesian approach

Abstract

Most published research on the comparison between medical treatment options merely compares the results (effectiveness and cost) obtained for each treatment group. The present work proposes the incorporation of other patient characteristics into the analysis. Most of the studies carried out in this context assume normality of both costs and effectiveness. In practice, however, the data are not always distributed according to this assumption. Altervative models have to be developed. In this paper, we present a general model of cost-effectiveness, incorporating both binary effectiveness and skewed cost. In a practical application, we compare two highly active antiretroviral treatments applied to asymptomatic HIV patients. We propose a logit model when the effectiveness is measured depending on whether an initial purpose is achieved. For this model, the measure to compare treatments is the difference in the probability of success. Besides, the cost data usually present a right skewing. We propose the use of the log-transformation to carry out the regression model. The three models are fitted demonstrating the advantages of this modelling. The cost-effectiveness acceptability curve is used as a measure for decision-making.