Luo Xiaohui, Boos Dennis D, Tamura Roy N
Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA.
Stat Med. 2004 Dec 15;23(23):3581-91. doi: 10.1002/sim.1800.
When only a certain proportion of subjects respond to treatment ('responders') or may never experience an event of interest (thus 'cured'), mixture models often lead to increased understanding of the treatment or disease process. This paper focuses on hypothesis testing in a dose-response framework and shows that increased power is possible by using a mixture model where both the logit of the response rate and the response mean are linear functions of the dose level. Three score tests are developed for testing an overall effect and permutation methods are used to control the type I error. Extensive simulations establish the power properties of the tests and show that our proposed score test has the best performance. The approach is illustrated by a multi-country clinical trial of rapid acting Intramuscular Olanzapine.
当只有一定比例的受试者对治疗有反应（“反应者”）或可能永远不会经历感兴趣的事件（因此“治愈”）时，混合模型通常有助于加深对治疗或疾病过程的理解。本文聚焦于剂量反应框架下的假设检验，并表明通过使用一种混合模型，即反应率的对数几率和反应均值均为剂量水平的线性函数，有可能提高检验效能。开发了三种计分检验来检验总体效应，并使用置换方法来控制I型错误。广泛的模拟确定了检验的效能特性，并表明我们提出的计分检验具有最佳性能。通过一项多国快速起效肌内注射奥氮平的临床试验对该方法进行了说明。
