Do health economic evaluations using observational data provide reliable assessment of treatment effects?

Economic evaluation in modern health care systems is seen as a transparent scientific framework that can be used to advance progress towards improvements in population health at the best possible value. Despite the perceived superiority that trial-based studies have in terms of internal validity, economic evaluations often employ observational data. In this review, the interface between econometrics and economic evaluation is explored, with emphasis placed on highlighting methodological issues relating to the evaluation of cost-effectiveness within a bivariate framework. Studies that satisfied the eligibility criteria exemplified the use of matching, regression analysis, propensity scores, instrumental variables, as well as difference-in-differences approaches. All studies were reviewed and critically appraised using a structured template. The findings suggest that although state-of-the-art econometric methods have the potential to provide evidence on the causal effects of clinical and policy interventions, their application in economic evaluation is subject to a number of limitations. These range from no credible assessment of key assumptions and scarce evidence regarding the relative performance of different methods, to lack of reporting of important study elements, such as a summary outcome measure and its associated sampling uncertainty. Further research is required to better understand the ways in which observational data should be analysed in the context of the economic evaluation framework.