Yang Yang, Zhao Jiwei, Wilding Gregory, Kluczynski Melissa, Bisson Leslie
AbbVie Inc., North Chicago, Illinois, United States.
Department of Biostatistics, School of Public Health and Health Professions, State University of New York at Buffalo, Buffalo, New York, United States.
J Appl Stat. 2020;47(5):827-843. doi: 10.1080/02664763.2019.1658727. Epub 2019 Aug 24.
This paper is motivated by the analytical challenges we encounter when analyzing the ChAMP (Chondral Lesions And Meniscus Procedures) study, a randomized controlled trial to compare debridement to observation of chondral lesions in arthroscopic knee surgery. The main outcome, WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) pain score, is derived from the patient's responses to the questionnaire collected in the study. The major goal is to identify potentially important variables that contribute to this outcome. In this paper, the model of interest is a semiparametric model for the pain score. To address the missing data issue, we adopt a flexible missingness mechanism which is much more versatile in practice than a single parametric model. Then we propose a pairwise conditional likelihood approach to estimate the unknown parameter in the semiparametric model without the need of modeling its nonparametric counterpart nor the missingness mechanism. For variable selection we apply a regularization approach with a variety of stability enhanced tuning parameter selection methods. We conduct comprehensive simulation studies to evaluate the performance of the proposed method. We also apply the proposed method to the ChAMP study to demonstrate its usefulness.
本文的动机源于我们在分析ChAMP（软骨损伤与半月板手术）研究时遇到的分析挑战，该研究是一项随机对照试验，旨在比较关节镜膝关节手术中软骨损伤清理术与观察法的效果。主要结局指标——WOMAC（西安大略和麦克马斯特大学骨关节炎指数）疼痛评分，源自患者对研究中收集的问卷的回答。主要目标是识别对该结局有潜在重要影响的变量。在本文中，感兴趣的模型是疼痛评分的半参数模型。为解决缺失数据问题，我们采用了一种灵活的缺失机制，在实际应用中比单一参数模型更具通用性。然后，我们提出了一种成对条件似然方法，用于估计半参数模型中的未知参数，而无需对其非参数部分或缺失机制进行建模。对于变量选择，我们应用了一种正则化方法，并采用了多种稳定性增强的调优参数选择方法。我们进行了全面的模拟研究，以评估所提出方法的性能。我们还将所提出的方法应用于ChAMP研究，以证明其有效性。
