Bayesian estimation, simulation and uncertainty analysis: the cost‐effectiveness of ganciclovir prophylaxis in liver transplantation
展示了将贝叶斯估计与模拟结合分析临床试验成本效果数据的方法，用马尔可夫链蒙特卡洛估计模型参数，模拟不同情景下新疗法的相对成本效果，并直接评估参数不确定性对决策的影响。
This paper demonstrates the usefulness of combining simulation with Bayesian estimation methods in analysis of cost-effectiveness data collected alongside a clinical trial. Specifically, we use Markov Chain Monte Carlo (MCMC) to estimate a system of generalized linear models relating costs and outcomes to a disease process affected by treatment under alternative therapies. The MCMC draws are used as parameters in simulations which yield inference about the relative cost-effectiveness of the novel therapy under a variety of scenarios. Total parametric uncertainty is assessed directly by examining the joint distribution of simulated average incremental cost and effectiveness. The approach allows flexibility in assessing treatment in various counterfactual premises and quantifies the global effect of parametric uncertainty on a decision-maker's confidence in adopting one therapy over the other.