Temkin N R
Biometrics. 1978 Dec;34(4):571-80.
The evaluation of therapies for chronic diseases is often based on the frequency and/or the duration of improvement. Treated separately, these endpoints may give contradictory impressions of the efficacy of the therapy. We propose a more unified method of summarizing improvement-related data--the probability of being in response, i.e., improved, as a function of time. Although improvement is not the only endpoint considered in most trials and this function will not always provide a clear answer to the question of which treatment has better improvement-related characteristics, it does combine the information on several endpoints usually considered separately into a single easily interpreted item. This function is estimated using the method of maximum likelihood on a distribution-free stochastic model of times to improvement and failure. Censored observations are taken into account. A detailed example using data from a cancer clinical trial is presented.
对慢性病治疗方法的评估通常基于改善的频率和/或持续时间。若分别看待这些终点指标,可能会对治疗效果产生相互矛盾的印象。我们提出一种更统一的方法来总结与改善相关的数据——作为时间函数的处于缓解(即改善)状态的概率。尽管改善并非大多数试验中考虑的唯一终点指标,且该函数并不总能明确回答哪种治疗方法具有更好的与改善相关特征的问题,但它确实将通常分别考虑的几个终点指标的信息整合为一个易于解释的单一项目。该函数是在一个无分布的改善和失败时间随机模型上使用最大似然法估计得出的。截尾观察值也被纳入考虑。文中给出了一个使用癌症临床试验数据的详细示例。