Statistics Group, Netherlands Organization for Applied Scientific Research (TNO), Wassenaarseweg 56, Leiden, The Netherlands; Department of Psychology, Katholieke Universiteit Leuven, Tiensestraat 102 - bus 3713, Leuven, Belgium.
Stat Med. 2014 Jan 30;33(2):219-37. doi: 10.1002/sim.5933. Epub 2013 Aug 6.
When two alternative treatments (A and B) are available, some subgroup of patients may display a better outcome with treatment A than with B, whereas for another subgroup, the reverse may be true. If this is the case, a qualitative (i.e., disordinal) treatment-subgroup interaction is present. Such interactions imply that some subgroups of patients should be treated differently and are therefore most relevant for personalized medicine. In case of data from randomized clinical trials with many patient characteristics that could interact with treatment in a complex way, a suitable statistical approach to detect qualitative treatment-subgroup interactions is not yet available. As a way out, in the present paper, we propose a new method for this purpose, called QUalitative INteraction Trees (QUINT). QUINT results in a binary tree that subdivides the patients into terminal nodes on the basis of patient characteristics; these nodes are further assigned to one of three classes: a first for which A is better than B, a second for which B is better than A, and an optional third for which type of treatment makes no difference. Results of QUINT on simulated data showed satisfactory performance, with regard to optimization and recovery. Results of an application to real data suggested that, compared with other approaches, QUINT provided a more pronounced picture of the qualitative interactions that are present in the data.
当有两种可供选择的治疗方法（A 和 B）时，某些亚组患者可能会对 A 治疗的效果优于 B，而对另一些亚组患者来说，情况可能正好相反。如果是这样，就存在定性（即无序）的治疗亚组相互作用。这种相互作用意味着某些亚组患者应该接受不同的治疗，因此最适合个性化医疗。在随机临床试验数据中，如果有许多可能以复杂方式与治疗相互作用的患者特征，那么目前还没有一种合适的统计方法来检测定性的治疗亚组相互作用。作为一种解决方案，在本文中，我们为此提出了一种新方法，称为 QUalitative INteraction Trees（QUINT）。QUINT 生成一个二叉树，根据患者特征将患者细分为终端节点；这些节点进一步被分配到三个类别之一：第一个类别表示 A 优于 B，第二个类别表示 B 优于 A，第三个类别表示治疗类型没有区别。QUINT 在模拟数据上的结果表现出令人满意的优化和恢复性能。对实际数据的应用结果表明，与其他方法相比，QUINT 更能清晰地描绘出数据中存在的定性相互作用。
