Model-Based Recursive Partitioning for Subgroup Analyses
Model-Based Recursive Partitioning for Subgroup Analyses
复制标题
DOI：
10.1515/ijb-2015-0032
复制
发表时间：
2016-05-01
影响因子：
1.2
通讯作者：
Hothorn, Torsten
中科院分区：
文献类型：
--
作者：
Seibold, Heidi;Zeileis, Achim;Hothorn, Torsten
识别具有不同治疗效果的患者亚组是实现个体化治疗的第一步。EMA的现行指南草案讨论了亚组分析中的潜力和问题，并制定了开发适当统计程序的挑战，以用于数据驱动的患者亚组识别。我们介绍了基于模型的递归分割作为一个程序的自动检测的患者亚组，可识别的预测因素。该方法从研究方案中主要分析定义的总体治疗效应模型开始，并使用检测该治疗效应中参数不稳定性的指标。该过程产生一个分段模型，其中具有与每个患者亚组对应的差异治疗参数。通过决策树将子组与预测因素联系起来。该方法适用于搜索患有肌萎缩性侧索硬化症的患者亚组，这些患者的阿曲唑治疗效果不同，阿曲唑是目前唯一批准用于这种疾病的药物。
The identification of patient subgroups with differential treatment effects is the first step towards individualised treatments. A current draft guideline by the EMA discusses potentials and problems in subgroup analyses and formulated challenges to the development of appropriate statistical procedures for the data-driven identification of patient subgroups. We introduce model-based recursive partitioning as a procedure for the automated detection of patient subgroups that are identifiable by predictive factors. The method starts with a model for the overall treatment effect as defined for the primary analysis in the study protocol and uses measures for detecting parameter instabilities in this treatment effect. The procedure produces a segmented model with differential treatment parameters corresponding to each patient subgroup. The subgroups are linked to predictive factors by means of a decision tree. The method is applied to the search for subgroups of patients suffering from amyotrophic lateral sclerosis that differ with respect to their Riluzole treatment effect, the only currently approved drug for this disease.