Estimating the cost-effectiveness of an intervention in a clinical trial when partial cost information is available: a Bayesian approach.

There is an increasing need to establish whether health-care interventions are cost effective as well as clinically effective. It is becoming increasingly common for cost studies to be incorporated into clinical trials, either on all patients or more usually on a subset of patients. Establishing the total cost per patient is complex, as it requires information on resource use, which may come from a variety of different sources. This complexity may lead to considerable missing data, and can result in some patients only having partial cost information. In this paper we consider a clinical trial consisting of 351 patients with advanced non-small cell lung cancer comparing chemotherapy with standard palliative care. A subset of 115 patients was selected for the cost sub-study. Total cost was split into four components, for which resource use was collected. Complete resource data were available on 82 patients. For the remaining patients at least one of the cost components was missing. The objective of this paper is to develop a Bayesian approach which simultaneously models both the clinical effectiveness data and the cost data, by modelling the individual components. This also provides estimates of the cost-effectiveness in terms of the Incremental Net Monetary Benefit (INMB) and Cost-Effectiveness Acceptability Curves (CEAC). We compare a number of different models of increasing complexity. The models estimate the interrelationships between the four cost components and survival, and thus enable a predictive distribution for each missing cost item to be obtained.