Open AccessA flexible semiparametric modeling approach for doubly censored data with an application to prostate cancer
Author(s) -
Seungbong Han,
Adin Cristian Andrei,
KamWah Tsui
Publication year - 2013
Publication title -
statistical methods in medical research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.952
H-Index - 85
eISSN - 1477-0334
pISSN - 0962-2802
DOI - 10.1177/0962280213498325
Subject(s) - jackknife resampling , covariate , semiparametric regression , estimator , estimating equations , semiparametric model , nonparametric statistics , computer science , prostate cancer , statistics , econometrics , regression , inference , mathematics , cancer , medicine , artificial intelligence
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.