LAUSR

Prospective economic evaluations conducted alongside clinical trials have become an increasingly popular approach in evaluating the cost-effectiveness of a public health initiative or treatment intervention. These types of economic studies provide improved internal validity and accuracy of cost and effectiveness estimates of health interventions and have the advantage of jointly observing health and economics outcomes of trial participants compared to simulation or decision-analytic models. However, missing data due to incomplete response or patient attrition, and sampling uncertainty are common concerns in econometric analysis of clinical trials. Missing data is a particular problem for comparative effectiveness trials of substance use disorder interventions. Methods such as multiple imputation and inverse probability weighting are two widely-recommend methods to address missing data bias, and the nonparametric bootstrap is recommended to address uncertainty in predicted mean cost and effectiveness between trial interventions. While these methods have been studied extensively by themselves, little is known on how to appropriately combine them, and the potential pitfalls and advantages of different approaches. We provide a timely review of statistical methods employed by 29 economic evaluations of substance use disorders intervention identified from 4 prior-published systematic reviews and a targeted search of the literature. We evaluate how each study addressed missing data bias, whether the recommended nonparametric bootstrap was employed, how these two methods were combined, and conclude with recommendations for future research.