Estimating Regression Parameters in an Extended Proportional Odds Model

• 成果类型: Article
• 署名作者: Chen, Ying Qing; Hu, Nan; Cheng, Su-Chun; Musoke, Philippa; Zhao, Lue Ping
• 署名单位: Utah System of Higher Education; University of Utah; Harvard University; Harvard University Medical Affiliates; Dana-Farber Cancer Institute; Makerere University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2012.656021
• 发表日期: 2012
• 页码: 318-330
• 关键词: transformation models; Semiparametric Inference; efficient estimation; hazards; transmission; nevirapine; hiv-1

摘要

The proportional odds model may serve as a useful alternative to the Cox proportional hazards model to study association between covariates and their survival functions in medical studies. In this article, we study an extended proportional odds model that incorporates the so-called external time-varying covariates. In the extended model, regression parameters have a direct interpretation of comparing survival functions, without specifying the baseline survival odds function. Semiparametric and maximum likelihood estimation procedures are proposed to estimate the extended model. Our methods are demonstrated by Monte Carlo simulations, and applied to a landmark randomized clinical trial of a short-course nevirapine (NVP) for mother-to-child transmission (MTCT) of human immunodeficiency virus type-1 (HIV-1). Additional application includes an analysis of the well-known Veterans Administration (VA) lung cancer trial.