O'Quigley John, Paoletti Xavier
Department of Biostatistics, Institut Curie, 75005 Paris, France.
Biometrics. 2003 Jun;59(2):430-40. doi: 10.1111/1541-0420.00050.
We investigate the two-group continual reassessment method for a dose-finding study in which we anticipate some ordering between the groups. This is a situation in which, for either group, we have little or almost no knowledge about which of the available dose levels will correspond to the maximum tolerated dose (MTD), but we may have quite strong knowledge concerning which of the two groups will have the higher level of MTD, if indeed they do not have the same MTD. The motivation for studying this problem came from an investigation into a new therapy for acute leukemia in children. The background to this study is discussed. There were two groups of patients: one group already received heavy prior therapy while the second group had received relatively much lighter prior therapy. It was therefore anticipated that the second group would have an MTD higher or at least as high as the first. Generally, likelihood methods or, equivalently, the use of noninformative Bayes priors, can be used to model the main aspects of the study, i.e., the MTD for one of the groups, reserving more informative Bayes modeling to be applied to the secondary features of the study. These secondary features may simply be the direction of the difference between the MTD levels for the two groups or, possibly, information on the potential gap between the two MTDs.
我们研究了用于剂量探索研究的两组连续重新评估方法,在此研究中我们预期两组之间存在某种排序。在这种情况下,对于任何一组,我们对于可用剂量水平中哪一个将对应最大耐受剂量(MTD)知之甚少或几乎一无所知,但如果两组的MTD确实不同,我们可能对哪一组的MTD水平更高有相当确凿的了解。研究这个问题的动机来自于对一种儿童急性白血病新疗法的调查。本文讨论了该研究的背景。有两组患者:一组已经接受了大量的前期治疗,而第二组接受的前期治疗相对较轻。因此预计第二组的MTD会高于或至少与第一组一样高。一般来说,似然方法或者等效地使用非信息性贝叶斯先验,可以用来对研究的主要方面进行建模,即其中一组的MTD,而保留更多信息性的贝叶斯建模用于研究的次要特征。这些次要特征可能仅仅是两组MTD水平之间差异的方向,或者可能是关于两个MTD之间潜在差距的信息。