Heitjan Daniel F, Li Huiling
Department of Biostatistics & Epidemiology, University of Pennsylvania, Philadelphia 19104-6021, USA.
Health Econ. 2004 Feb;13(2):191-8. doi: 10.1002/hec.825.
We describe a method for estimating the cost-effectiveness of a new treatment compared to a standard, using data from a comparative clinical trial. We quantify the clinical effectiveness as a binary variable indicating success or failure. The underlying statistical model assumes that costs are uncensored and follow separate gamma distributions in each of the groups defined by the four possible combinations of treatment arm and effectiveness outcome. The method is subjectivist, in that it represents prior uncertainty about model parameters with a probability distribution, which we update via Bayes's theorem to produce a posterior distribution. We approximate the posterior by importance sampling, a straightforward simulation method. We illustrate the method with an analysis of cost (derived from resource usage data) and effectiveness (measured by one-year survival) in a clinical trial in heart disease. The example demonstrates that the method is practical and provides for a flexible data analysis.
我们描述了一种使用来自比较临床试验的数据来估计新治疗方法与标准治疗方法相比的成本效益的方法。我们将临床有效性量化为一个二元变量，表明成功或失败。基本统计模型假设成本是无删失的，并且在由治疗组和有效性结果的四种可能组合定义的每个组中遵循单独的伽马分布。该方法是主观的，因为它用概率分布表示模型参数的先验不确定性，并通过贝叶斯定理进行更新以产生后验分布。我们通过重要性抽样（一种简单的模拟方法）来近似后验分布。我们通过对心脏病临床试验中的成本（源自资源使用数据）和有效性（通过一年生存率衡量）进行分析来说明该方法。该示例表明该方法是实用的，并提供了灵活的数据分析。
