Statistical Modeling for Health Economic Evaluations

Abstract

Health economic evaluation has become increasingly important in medical research and recently has been built on solid statistical and decision-theoretic foundations, particularly under the Bayesian approach. In this article we review the basic concepts and issues associated with the statistical and decision-theoretic components of health economic evaluations. We present examples of typical models used in different contexts (depending on the availability of data). We also describe the process of uncertainty analysis, a crucial component of economic evaluations for health care interventions, aimed at assessing the impact of uncertainty in the model parameters on the final decision-making process. Finally, we discuss some of the most recent methodological developments, related with the application of advanced statistical models (e.g., Gaussian process regression) to facilitate the application of computationally expensive tools such as value of information analysis.