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序贯剂量探索设计的回顾性分析。

Retrospective analysis of sequential dose-finding designs.

作者信息

O'Quigley John

机构信息

Institut Curie, 26 rue d'Ulm, 75005 Paris, France

出版信息

Biometrics. 2005 Sep;61(3):749-56. doi: 10.1111/j.1541-0420.2005.00353.x.

Abstract

The continual reassessment method (CRM) is a dose-finding design using a dynamic sequential updating scheme. In common with other dynamic schemes the method estimates a current dose level corresponding to some target percentile for experimentation. The estimate is based on all included subjects. This continual reevaluation is made possible by the use of a simple model. As it stands, neither the CRM, nor any of the other dynamic schemes, allow for the correct estimation of some target percentile, based on retrospective data apart from the exceptional situation in which the simplified model exactly generates the observations. In this article we focus on the very specific issue of retrospective analysis of data generated by some arbitrary mechanism and subsequently analyzed via the continual reassessment method. We show how this can be done consistently. The proposed methodology is not restricted to that particular design and is applicable to any sequential updating scheme in which dose levels are associated with percentiles via model inversion.

摘要

连续重新评估法(CRM)是一种采用动态序贯更新方案的剂量探索设计。与其他动态方案一样,该方法估计与某个目标百分位数相对应的当前剂量水平用于试验。该估计基于所有纳入的受试者。通过使用一个简单模型使得这种连续重新评估成为可能。就目前而言,无论是CRM还是任何其他动态方案,除了简化模型恰好生成观测值的特殊情况外,都无法基于回顾性数据正确估计某个目标百分位数。在本文中,我们关注由某种任意机制生成并随后通过连续重新评估法进行分析的数据的回顾性分析这一非常具体的问题。我们展示了如何一致地做到这一点。所提出的方法不限于该特定设计,适用于通过模型反演将剂量水平与百分位数相关联的任何序贯更新方案。

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