Vanness David J, Kim W Ray
Division of Health Care Policy & Research, Mayo Clinic, Rochester, Minnesota 55905, USA.
Health Econ. 2002 Sep;11(6):551-66. doi: 10.1002/hec.739.
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.
本文展示了在分析与一项临床试验同时收集的成本效益数据时，将模拟与贝叶斯估计方法相结合的有用性。具体而言，我们使用马尔可夫链蒙特卡罗（MCMC）方法来估计一个广义线性模型系统，该系统将成本和结果与在替代疗法下受治疗影响的疾病过程相关联。MCMC抽样用作模拟中的参数，这些模拟得出关于在各种情况下新疗法相对成本效益的推断。通过检查模拟平均增量成本和效果的联合分布直接评估总参数不确定性。该方法在评估各种反事实前提下的治疗时具有灵活性，并量化了参数不确定性对决策者选择一种疗法而非另一种疗法的信心的总体影响。
