Theoretical study of the continual reassessment method
Theoretical study of the continual reassessment method
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DOI：
10.1016/j.jspi.2005.08.003
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发表时间：
2006-06-01
影响因子：
0.9
通讯作者：
O'Quigley, J
中科院分区：
文献类型：
--
作者：
O'Quigley, J
持续再评估法（continuous reassessment method， CRM）是由O'Quigley等人[1990]首次提出的。持续再评估方法：癌症I期临床试验的实用设计。生物识别技术[j]。随后有许多文章补充了原有的观点，其中包括Babb等人[1998]的文章。癌症I期临床试验：有效剂量递增与过量控制。中央集权。中华医学杂志，2002,18(3):387 - 398。二变量连续重评价方法。将CRM扩展到两个相互竞争结果的I期试验。中国控制。中国科学进展[j]，[1993]。癌症I期临床试验的持续再评估方法：模拟研究。中央集权。中华医学杂志，2009,32(3):391 - 398。一期癌症临床试验持续再评估方法的实际修改。j . Biopharm。[j] .中国农业科学，2014,32(1):1 - 4。一期研究持续再评估方法的一些实际改进。中央集权。[j] .中国生物医学工程学报，2009,31(4):349 - 361。持续再评估方法及其应用：一期癌症临床试验的贝叶斯方法。中央集权。[j] .中华医学杂志，2006,26(2):481 - 481。采用连续再评估方法进行一期试验的纵向设计。中国控制。[j] .中国农业科学[j]。应用临床前数据启动最大耐受剂量发现试验的改进的连续重新评估方法。j .中国。药物学杂志，1999,19(2):1 - 4。连续重新评估方法的延伸，在癌症患者的剂量发现研究中使用初步的上下设计，以便调查更多的剂量水平。中央集权。[j] .中华医学杂志，2006,32(1):1- 2。估计在癌症I期临床试验后推荐剂量的毒性概率。生物识别技术[j] .中国生物医学工程学报，2009,33(4):558 - 562。持续重新评估方法：一种可能性方法。生物计量学[j] .,(1999),[2002]。剂量测定研究中的非参数优化设计。生物统计学[j] .安徽农业大学学报（自然科学版），2003。有序群体的连续重评价方法。生物识别技术[j] .中国生物医学工程学报，2009,29(4):429-439。[1998]剂量发现试验中改进的连续再评估方法的实际实施。癌症Chemother。[j] .中华医学杂志，2001,19(4):429-436。剂量发现研究中基于逻辑回归模型的贝叶斯决策程序。j . Biopharm。统计学家。8,445-467]。Storer[1989]对该方法进行了广泛的描述。I期临床试验的设计和分析。生物医学工程学报[j]。无论是基于似然还是基于贝叶斯，考虑到工作模型被参数化，推理在理论上都存在特殊的困难。尽管如此，客户关系管理模型已经证明了自己的实际用途，在这项工作中，目的是将重点放在支撑该方法的主要理论思想上，获得可以在实践中提供指导的结果。从这个理论框架中产生了许多结果和一些进一步的发展，特别是构建随机分配受试者的方法以及处理患者异质性问题的更稳健的方法。(c) 2005年Elsevier B.V.出版
The continual reassessment method (CRM) was first introduced by O'Quigley et al. [1990. Continual reassessment method: a practical design for Phase I clinical trials in cancer. Biometrics 46, 33-48]. Many articles followed adding to the original ideas, among which are articles by Babb et al. [1998. Cancer Phase I clinical trials: efficient dose escalation with overdose control. Statist. Med. 17,1103-1120], Braun [2002. The bivariate-continual reassessment method. Extending the CRM to phase I trials of two competing outcomes. Controlled Clin. Trials 23, 240-256], Chevret [1993. The continual reassessment method in cancer phase I clinical trials: a simulation study. Statist. Med. 12, 1093-1108], Faries [1994. Practical modifications of the continual reassessment method for phase I cancer clinical trials. J. Biopharm. Statist. 4, 147-164], Goodman et al. [1995. Some practical improvements in the continual reassessment method for phase I studies. Statist. Med. 14, 1149-1161], Ishizuka and Ohashi [2001. The continual reassessment method and its applications: a Bayesian methodology for phase I cancer clinical trials. Statist. Med. 20, 2661-2681], Legedeza and Ibrahim [2002. Longitudinal design for phase I trials using the continual reassessment method. Controlled Clin. Trials 21, 578-588], Mahmood [2001. Application of preclinical data to initiate the modified continual reassessment method for maximum tolerated dose-finding trial. J. Clin. Pharmacol. 41, 19-24], Moller [1995. An extension of the continual reassessment method using a preliminary up and down design in a dose finding study in cancer patients in order to investigate a greater number of dose levels. Statist. Med. 14, 911-922], O'Quigley [1992. Estimating the probability of toxicity at the recommended dose following a Phase I clinical trial in cancer. Biometrics 48, 853-862], O'Quigley and Shen [1996. Continual reassessment method: a likelihood approach. Biometrics 52, 163-174], O'Quigley et al. (1999), O'Quigley et al. [2002. Non-parametric optimal design in dose finding studies. Biostatistics 3, 51-56], O'Quigley and Paoletti [2003. Continual reassessment method for ordered groups. Biometrics 59, 429-439], Piantodosi et al., 1998. [1998 Practical implementation of a modified continual reassessment method for dose-finding trials. Cancer Chemother. Pharmacol. 41, 429-436] and Whitehead and Williamson [1998. Bayesian decision procedures based on logistic regression models for dose-finding studies. J. Biopharm. Statist. 8, 445-467]. The method is broadly described by Storer [1989. Design and analysis of Phase I clinical trials. Biometrics 45, 925-937]. Whether likelihood or Bayesian based, inference poses particular theoretical difficulties in view of working models being under-parameterized. Nonetheless CRM models have proven themselves to be of practical use and, in this work, the aim is to turn the spotlight on the main theoretical ideas underpinning the approach, obtaining results which can provide guidance in practice. Stemming from this theoretical framework are a number of results and some further development, in particular the way to structure a randomized allocation of subjects as well as a more robust approach to the problem of dealing with patient heterogeneity. (c) 2005 Published by Elsevier B.V.