Estimating Regression Parameters in an Extended Proportional Odds Model
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Date
2012
Journal Title
Journal ISSN
Volume Title
Publisher
Journal of the American Statistical Association
Abstract
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.
Description
Keywords
Counting process; Estimating function; HIV/AIDS; Maximum likelihood estimation; Semiparametric model; Time-varying covariate
Citation
Chen, Y. Q., Hu, N., Cheng, S. C., Musoke, P., & Zhao, L. P. (2012). Estimating regression parameters in an extended proportional odds model. Journal of the American Statistical Association, 107(497), 318-330.https://doi.org/10.1080/01621459.2012.656021