Zhou Zehua, Zhao Jiwei, Kluczynski Melissa
Department of Biostatistics, School of Public Health and Health Professions, State University of New York at Buffalo, 3435 Main Street, Buffalo, NY 14214, United States.
Department of Orthopaedics, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, 462 Grider Street, Buffalo, NY 14215, United States.
Stat Theory Relat Fields. 2019;2019. doi: 10.1080/24754269.2019.1587692. Epub 2019 Mar 19.
With the improved knowledge on clinical relevance and more convenient access to the patient-reported outcome data, clinical researchers prefer to adopt minimal clinically important difference (MCID) rather than statistical significance as a testing standard to examine the effectiveness of certain intervention or treatment in clinical trials. A practical method to determining the MCID is based on the diagnostic measurement. By using this approach, the MCID can be formulated as the solution of a large margin classification problem. However, this method only produces the point estimation, hence lacks of ways to evaluate its performance. In this paper we introduce an -out-of- bootstrap approach which provides the interval estimations for MCID and its classification error, an associated accuracy measure for performance assessment. A variety of extensive simulation studies are implemented to show the advantages of our proposed method. Analysis of the chondral lesions and meniscus procedures (ChAMP) trial is our motivating example and is used to illustrate our method.
随着对临床相关性的认识不断提高，以及获取患者报告结局数据变得更加便捷，临床研究人员在临床试验中更倾向于采用最小临床重要差异（MCID）而非统计学显著性作为检验标准，以考察某些干预措施或治疗方法的有效性。一种确定MCID的实用方法基于诊断测量。通过使用这种方法，MCID可以被表述为一个大间隔分类问题的解。然而，这种方法仅产生点估计，因此缺乏评估其性能的方法。在本文中，我们引入了一种留一法自助抽样方法，该方法为MCID及其分类误差提供区间估计，这是一种用于性能评估的相关准确性度量。我们进行了各种广泛的模拟研究以展示我们所提出方法的优势。软骨损伤与半月板手术（ChAMP）试验是我们的激励示例，并用于阐述我们的方法。
