Cost-Effectiveness Analysis Alongside Clinical Trials

Over the past quarter century, as costs have become an important factor in medical decision making, indices of cost-effectiveness (CE) have increasingly been used in the evaluation of medical therapies. The traditional approach to cost-effectiveness analysis (CEA) utilizes decision-analytic models based on estimates of cost and effectiveness outcomes obtained from nonsampled secondary data (from the literature, insurance claims databases, and expert opinion) and employs sensitivity analysis to examine the variability in results as uncertain model inputs are varied over reasonable ranges. Throughout the past decade, there has been an increasing trend for economic studies to be incorporated into large clinical trials. This has allowed for cost-effectiveness to be evaluated directly, using primary patient-level data on both clinical outcomes and costs. The stochastic, or random, nature of this data, resulting from patient-to-patient (sampling) variability, allows for uncertainty associated with estimates of cost-effectiveness to be estimated using methods of statistical analysis. This chapter presents an overview of statistical and other methodological considerations in the evaluation of cost-effectiveness using experimentally obtained data from randomized controlled trials.