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在癌症剂量探索研究中确定最成功剂量(MSD)。

Identifying the most successful dose (MSD) in dose-finding studies in cancer.

作者信息

Zohar Sarah, O'Quigley John

机构信息

Centre d'Investigation Clinique, U717 INSERM, Department de Biostatistique et Informatique Medicale, Hôpital Saint-Louis, Paris, France.

出版信息

Pharm Stat. 2006 Jul-Sep;5(3):187-99. doi: 10.1002/pst.209.

DOI:10.1002/pst.209
PMID:17080752
Abstract

For a dose finding study in cancer, the most successful dose (MSD), among a group of available doses, is that dose at which the overall success rate is the highest. This rate is the product of the rate of seeing non-toxicities together with the rate of tumor response. A successful dose finding trial in this context is one where we manage to identify the MSD in an efficient manner. In practice we may also need to consider algorithms for identifying the MSD which can incorporate certain restrictions, the most common restriction maintaining the estimated toxicity rate alone below some maximum rate. In this case the MSD may correspond to a different level than that for the unconstrained MSD and, in providing a final recommendation, it is important to underline that it is subject to the given constraint. We work with the approach described in O'Quigley et al. [Biometrics 2001; 57(4):1018-1029]. The focus of that work was dose finding in HIV where both information on toxicity and efficacy were almost immediately available. Recent cancer studies are beginning to fall under this same heading where, as before, toxicity can be quickly evaluated and, in addition, we can rely on biological markers or other measures of tumor response. Mindful of the particular context of cancer, our purpose here is to consider the methodology developed by O'Quigley et al. and its practical implementation. We also carry out a study on the doubly under-parameterized model, developed by O'Quigley et al. but not

摘要

对于癌症剂量探索研究,在一组可用剂量中,最成功剂量(MSD)是指总体成功率最高的那个剂量。该成功率是无毒性发生率与肿瘤反应率的乘积。在此背景下,一个成功的剂量探索试验是指我们能够以高效方式识别出MSD的试验。在实际操作中,我们可能还需要考虑用于识别MSD的算法,这些算法可以纳入某些限制条件,最常见的限制条件是仅将估计的毒性率维持在某个最大率以下。在这种情况下,MSD可能对应于与无约束MSD不同的水平,并且在给出最终建议时,重要的是要强调它受到给定约束的限制。我们采用O'Quigley等人[《生物统计学》2001年;57(4):1018 - 1029]中描述的方法。该工作的重点是HIV中的剂量探索,在那里毒性和疗效信息几乎能立即获得。近期的癌症研究也开始属于同一范畴,和以前一样,毒性可以快速评估,此外,我们还可以依靠生物标志物或其他肿瘤反应指标。考虑到癌症的特殊背景,我们这里的目的是考虑O'Quigley等人开发的方法及其实际应用。我们还对O'Quigley等人开发的双重参数不足模型进行了研究,但未……

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