Kikuchi Takashi, Gittins John
Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, U K.
Stat Med. 2009 Aug 15;28(18):2293-306. doi: 10.1002/sim.3630.
It is necessary for the calculation of sample size to achieve the best balance between the cost of a clinical trial and the possible benefits from a new treatment. Gittins and Pezeshk developed an innovative (behavioral Bayes) approach, which assumes that the number of users is an increasing function of the difference in performance between the new treatment and the standard treatment. The better a new treatment, the more the number of patients who want to switch to it. The optimal sample size is calculated in this framework. This BeBay approach takes account of three decision-makers, a pharmaceutical company, the health authority and medical advisers. Kikuchi, Pezeshk and Gittins generalized this approach by introducing a logistic benefit function, and by extending to the more usual unpaired case, and with unknown variance. The expected net benefit in this model is based on the efficacy of the new drug but does not take account of the incidence of adverse reactions. The present paper extends the model to include the costs of treating adverse reactions and focuses on societal cost-effectiveness as the criterion for determining sample size. The main application is likely to be to phase III clinical trials, for which the primary outcome is to compare the costs and benefits of a new drug with a standard drug in relation to national health-care.
为了在临床试验成本与新疗法可能带来的益处之间实现最佳平衡，计算样本量是很有必要的。吉廷斯和佩泽什克开发了一种创新的（行为贝叶斯）方法，该方法假定使用者数量是新疗法与标准疗法之间性能差异的递增函数。新疗法越好，想要转而使用它的患者数量就越多。在此框架内计算最优样本量。这种贝叶斯方法考虑了三个决策者，即制药公司、卫生当局和医学顾问。菊池、佩泽什克和吉廷斯通过引入逻辑效益函数、扩展到更常见的非配对情况以及方差未知的情况，对该方法进行了推广。此模型中的预期净效益基于新药的疗效，但未考虑不良反应的发生率。本文将该模型进行扩展，以纳入治疗不良反应的成本，并将社会成本效益作为确定样本量的标准。其主要应用可能是针对III期临床试验，对于此类试验，主要结果是就国家医疗保健而言，比较新药与标准药物的成本和效益。
