O'Quigley J, Shen L Z
Unité 436 INSERM, Paris, France.
Biometrics. 1996 Jun;52(2):673-84.
The continual reassessment method as described by O'Quigley, Pepe, and Fisher (1990, Biometrics 46, 33-48) leans to a large extent upon a Bayesian methodology. Initial experimentation and sequential updating are carried out in a natural way within the context of a Bayesian framework. In this paper we argue that such a framework is easily changed to a more classic one leaning upon likelihood theory. The essential features of the continual reassessment method remain unchanged. In particular, large sample properties are the same unless the prior is degenerate. For small samples and as far as the final recommended dose level is concerned, simulations indicate that there is not much to choose between a likelihood approach and a Bayesian one. However, for in-trial allocation of dose levels to patients, there are some differences and these are discussed. In contrast to the Bayesian approach, a likelihood one requires some extra effort to get off the ground. This is because the likelihood equation has no solution until we observe a toxicity. Initially then we suggest working with either a standard Up-and-Down scheme or standard continual reassessment method until toxicity is observed and then switching to the new scheme.
奥奎利、佩佩和费舍尔（1990年，《生物统计学》46卷，33 - 48页）所描述的连续重新评估方法在很大程度上依赖于贝叶斯方法。初始实验和序贯更新在贝叶斯框架内以自然的方式进行。在本文中，我们认为这样一个框架可以很容易地转变为一个更经典的、依赖似然理论的框架。连续重新评估方法的基本特征保持不变。特别是，除非先验分布是退化的，否则大样本性质是相同的。对于小样本以及就最终推荐剂量水平而言，模拟表明在似然方法和贝叶斯方法之间没有太多可选择的。然而，对于在试验中将剂量水平分配给患者，存在一些差异并将对此进行讨论。与贝叶斯方法不同，似然方法需要一些额外的努力才能启动。这是因为在观察到毒性之前，似然方程没有解。那么最初我们建议使用标准的上下法或标准的连续重新评估方法，直到观察到毒性，然后再切换到新方法。
