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2022 | OriginalPaper | Buchkapitel

Case-Cohort Studies with Time-Dependent Covariates and Interval-Censored Outcome

verfasst von : Xiaoming Gao, Michael G. Hudgens, Fei Zou

Erschienen in: Emerging Topics in Modeling Interval-Censored Survival Data

Verlag: Springer International Publishing

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Abstract

In large cohort studies on rare diseases, the case-cohort design is widely used to assess associations between covariates and survival time (e.g., time until disease onset). In many settings, the event of interest is not observed at an exact time point but only known to occur between two study visits. In this chapter, we consider fitting parametric survival models to data from case-cohort studies with interval censored outcomes and both fixed and time-dependent covariates. Simulation results demonstrate the proposed estimator is approximately unbiased and the standard errors are well estimated from the sandwich estimators. The methods are applied to an observational study which examined the association between hormonal contraceptive use and risk of HIV acquisition.

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Vorheriges Kapitel Modeling and Analysis of Chronic Disease Processes Under Intermittent Observation

Nächstes Kapitel The BivarIntCensored: An R Package for Nonparametric Inference of Bivariate Interval-Censored Data

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Titel: Case-Cohort Studies with Time-Dependent Covariates and Interval-Censored Outcome
verfasst von: Xiaoming Gao
Michael G. Hudgens
Fei Zou
Verlag: Springer International Publishing
Buch: Emerging Topics in Modeling Interval-Censored Survival Data
Print ISBN: 978-3-031-12365-8

Electronic ISBN: 978-3-031-12366-5

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-031-12366-5_11

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