title: A flexible semiparametric modeling approach for doubly censored data with an application to prostate cancer.
abstract: Doubly censored data often arise in medical studies of disease progression involving two related events for which both an originating and a terminating event are interval-censored. Although regression modeling for such doubly censored data may be complicated, we propose a simple semiparametric regression modeling strategy based on jackknife pseudo-observations obtained using nonparametric estimators of the survival function. Inference is carried out via generalized estimating equations. Simulations studies show that the proposed method produces virtually unbiased covariate effect estimates, even for moderate sample sizes. A prostate cancer study example illustrates the practical advantages of the proposed approach.
authors.affiliation: Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea hanseungbong@amc.seoul.kr.
authors.forename: Seungbong
authors.initials: S
authors.lastname: Han
authors.affiliation: BCVI Clinical Trials Unit, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
authors.forename: Adin-Cristian
authors.initials: AC
authors.lastname: Andrei
authors.affiliation: Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA.
authors.forename: Kam-Wah
authors.initials: KW
authors.lastname: Tsui
mesh_terms: D017322:Clinical Trials, Phase II as Topic / Q000379:methods*; D018450:Disease Progression; D006801:Humans; D008297:Male; D015233:Models, Statistical; D016016:Proportional Hazards Models; D011471:Prostatic Neoplasms / Q000188:drug therapy*; D012044:Regression Analysis; D018401:Sample Size; D018709:Statistics, Nonparametric; D016019:Survival Analysis
keywords: doubly censored data; pseudo-observations; regression; semiparametric; survival analysis
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