Using covariates to reduce uncertainty in the economic evaluation of clinical trial data
提出一种贝叶斯方法，在卫生技术评估中使用协变量来减少治疗效应估计的不确定性，并通过模拟数据与未调整协变量的方法对比展示优势。
As part of their practice, policymakers have to make economic evaluations using clinical trial data. Recent interest has been expressed in determining how cost-effectiveness analysis can be undertaken in a regression framework. In this respect, published research basically provides a general method for prognostic factor adjustment in the presence of imbalance, emphasizing sub-group analysis. In this paper, we present an alternative method from a Bayesian approach. We propose the use of covariates in Bayesian health technology assessment in order to reduce uncertainty about the effect of treatments. We show its advantages by comparison with another published method that do not adjust for covariates using simulated data.