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肿瘤学中的剂量递增设计:自适应剂量递增设计(ADEPT)与连续重新评估方法(CRM)

Dose-escalation designs in oncology: ADEPT and the CRM.

作者信息

Shu Jianfen, O'Quigley John

机构信息

Division of Biostatistics and Epidemiology, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908-0717, USA.

出版信息

Stat Med. 2008 Nov 20;27(26):5345-53; discussion 5354-5. doi: 10.1002/sim.3403.

Abstract

The ADEPT software package is not a statistical method in its own right as implied by Gerke and Siedentop (Statist. Med. 2008; DOI: 10.1002/sim.3037). ADEPT implements two-parameter CRM models as described in O'Quigley et al. (Biometrics 1990; 46(1):33-48). All of the basic ideas (use of a two-parameter logistic model, use of a two-dimensional prior for the unknown slope and intercept parameters, sequential estimation and subsequent patient allocation based on minimization of some loss function, flexibility to use cohorts instead of one by one inclusion) are strictly identical. The only, and quite trivial, difference arises in the setting of the prior. O'Quigley et al. (Biometrics 1990; 46(1):33-48) used priors having an analytic expression whereas Whitehead and Brunier (Statist. Med. 1995; 14:33-48) use pseudo-data to play the role of the prior. The question of interest is whether two-parameter CRM works as well, or better, than the one-parameter CRM recommended in O'Quigley et al. (Biometrics 1990; 46(1):33-48). Gerke and Siedentop argue that it does. The published literature suggests otherwise. The conclusions of Gerke and Siedentop stem from three highly particular, and somewhat contrived, situations. Unlike one-parameter CRM (Biometrika 1996; 83:395-405; J. Statist. Plann. Inference 2006; 136:1765-1780; Biometrika 2005; 92:863-873), no statistical properties appear to have been studied for two-parameter CRM. In particular, for two-parameter CRM, the parameter estimates are inconsistent. This ought to be a source of major concern to those proposing its use. Worse still, for finite samples the behavior of estimates can be quite wild despite having incorporated the kind of dampening priors discussed by Gerke and Siedentop. An example in which we illustrate this behavior describes a single patient included at level 1 of 6 levels and experiencing a dose limiting toxicity. The subsequent recommendation is to experiment at level 6! Such problematic behavior is not common. Even so, we show that the allocation behavior of two-parameter CRM is very much less stable than that of one-parameter CRM.

摘要

ADEPT软件包本身并非一种统计方法,这与Gerke和Siedentop所暗示的(《统计医学》,2008年;DOI: 10.1002/sim.3037)不同。ADEPT实现了如O'Quigley等人所述的双参数CRM模型(《生物统计学》,1990年;46(1):33 - 48)。所有基本思想(使用双参数逻辑模型、对未知斜率和截距参数使用二维先验、基于最小化某些损失函数的序贯估计及后续患者分配、使用队列而非逐个纳入的灵活性)完全相同。唯一且相当细微的差异出现在先验的设定上。O'Quigley等人(《生物统计学》,1990年;46(1):33 - 48)使用了具有解析表达式的先验,而Whitehead和Brunier(《统计医学》,1995年;14:33 - 48)使用伪数据来充当先验的角色。感兴趣的问题是双参数CRM是否与O'Quigley等人(《生物统计学》,1990年;46(1):33 - 48)推荐的单参数CRM效果一样好,或者更好。Gerke和Siedentop认为它是这样的。但已发表的文献却表明并非如此。Gerke和Siedentop的结论源于三种非常特殊且有些人为设定的情况。与单参数CRM(《生物计量学》,1996年;83:395 - 405;《统计规划与推断杂志》,2006年;136:1765 - 1780;《生物计量学》,2005年;92:863 - 873)不同,似乎尚未对双参数CRM的统计性质进行研究。特别是对于双参数CRM,参数估计是不一致的。这对于提议使用它的人来说应该是一个主要担忧的来源。更糟糕的是,对于有限样本,尽管纳入了Gerke和Siedentop所讨论的那种阻尼先验,但估计值的行为可能会相当不稳定。我们用一个例子来说明这种行为,该例子描述了在6个剂量水平中的第1水平纳入的一名患者经历了剂量限制毒性。随后的建议是在第6水平进行试验!这种有问题的行为并不常见。即便如此,我们表明双参数CRM的分配行为比单参数CRM的稳定性要差得多。

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