Lipkovich Ilya, Dmitrienko Alex, Muysers Christoph, Ratitch Bohdana
a QuintilesIMS, Advisory Analytics , Durham , NC , USA.
b Mediana Inc , Overland Park , KS , USA.
J Biopharm Stat. 2018;28(1):63-81. doi: 10.1080/10543406.2017.1397009. Epub 2017 Nov 27.
The general topic of subgroup identification has attracted much attention in the clinical trial literature due to its important role in the development of tailored therapies and personalized medicine. Subgroup search methods are commonly used in late-phase clinical trials to identify subsets of the trial population with certain desirable characteristics. Post-hoc or exploratory subgroup exploration has been criticized for being extremely unreliable. Principled approaches to exploratory subgroup analysis based on recent advances in machine learning and data mining have been developed to address this criticism. These approaches emphasize fundamental statistical principles, including the importance of performing multiplicity adjustments to account for selection bias inherent in subgroup search. This article provides a detailed review of multiplicity issues arising in exploratory subgroup analysis. Multiplicity corrections in the context of principled subgroup search will be illustrated using the family of SIDES (subgroup identification based on differential effect search) methods. A case study based on a Phase III oncology trial will be presented to discuss the details of subgroup search algorithms with resampling-based multiplicity adjustment procedures.
由于亚组识别在定制疗法和个性化医疗的发展中具有重要作用，其总体主题在临床试验文献中备受关注。亚组搜索方法常用于后期临床试验，以识别具有某些理想特征的试验人群子集。事后或探索性亚组探索因极其不可靠而受到批评。基于机器学习和数据挖掘的最新进展，已开发出探索性亚组分析的原则性方法来应对这一批评。这些方法强调基本统计原则，包括进行多重性调整以考虑亚组搜索中固有的选择偏倚的重要性。本文详细回顾了探索性亚组分析中出现的多重性问题。将使用SIDES（基于差异效应搜索的亚组识别）方法族来说明原则性亚组搜索背景下的多重性校正。将呈现一个基于III期肿瘤学试验的案例研究，以讨论基于重采样的多重性调整程序的亚组搜索算法的细节。
