A propensity score approach to estimating the cost–effectiveness of medical therapies from observational data
提出一种线性模型框架，通过倾向性评分调整净货币收益，以减少观察性研究中基线差异导致的估计偏倚，并通过模拟和膀胱癌数据验证其稳健性。
Health summary measures are commonly used by policy makers to help make decisions on the allocation of societal resources for competing medical treatments. The net monetary benefit is a health summary measure that overcomes the statistical limitations of a popular measure namely, the cost-effectiveness ratio. We introduce a linear model framework to estimate propensity score adjusted net monetary benefit. This method provides less biased estimates in the presence of significant differences in baseline measures and demographic characteristics between treatment groups in quasi-randomized or observational studies. Simulation studies were conducted to better understand the utility of propensity score adjusted estimates of net monetary benefits when important covariates are unobserved. The results indicated that the propensity score adjusted net monetary benefit provides a robust measure of cost-effectiveness in the presence of hidden bias. The methods are illustrated using data from SEER-Medicare for the treatment of bladder cancer.