Estimating Regression Parameters in an Extended Proportional Odds Model

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dc.contributor.authorChen, Ying Qing
dc.contributor.authorHu, Nan
dc.contributor.authorCheng, Su-Chun
dc.contributor.authorMusoke, Philippa
dc.contributor.authorZhao, Lue Ping
dc.date.accessioned2022-01-31T14:22:44Z
dc.date.available2022-01-31T14:22:44Z
dc.date.issued2012
dc.description.abstractThe 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.en_US
dc.identifier.citationChen, 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/10.1080/01621459.2012.656021en_US
dc.identifier.issn0162-1459
dc.identifier.urihttps://nru.uncst.go.ug/xmlui/handle/123456789/1682
dc.language.isoenen_US
dc.publisherJournal of the American Statistical Associationen_US
dc.subjectCounting process; Estimating function; HIV/AIDS; Maximum likelihood estimation; Semiparametric model; Time-varying covariateen_US
dc.titleEstimating Regression Parameters in an Extended Proportional Odds Modelen_US
dc.typeArticleen_US
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