Modeling Survival Data: Extending the Cox Model

Therneau, Terry M.; Grambsch, Patricia M.

doi:10.1007/978-1-4757-3294-8

Cited by 8,674Statistical Methods and InferenceProbability and Statistical ResearchStatistical Distribution Estimation and Applications

Modeling Survival Data: Extending the Cox Model

Therneau, Terry M., Grambsch, Patricia M.·2000·Statistics for Biology and Health

DOI 10.1007/978-1-4757-3294-8

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In secondary analyses, regression analyses were redone using generalized estimating equations or marginal models to account for familial relatedness.
“in secondary analyses, aforementioned regression analyses were redone using either generalized estimating equations (GEE) (for linear regression models) [36], or using a marginal models approach (for Cox regression models) [37] to account for familial relatedness.”
From Comprehensive cross-sectional and longitudinal comparisons of plasma glial fibrillary acidic protein and neurofilament light across FTD spectrum disorders (2025) · cites this paper as [37]

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Authors: Therneau, T. M., & Grambsch, P. M.
Affiliations: Mayo Clinic (US)
University of Minnesota (US)
ORCID: Therneau, Terry M.
Venue: Statistics for Biology and Health
Publisher: Springer New York
Published: 2000
ISSN: 1431-8776 (ISSN-L)
DOI: 10.1007/978-1-4757-3294-8
OpenAlex: W1570622790
Cited by: 8,674
References: 0
Citations by year: 2026: 410 · 2025: 623 · 2024: 576 · 2023: 628 · 2022: 604 · 2021: 708 · 2020: 632 · 2019: 500 · 2018: 430 · 2017: 404 · 2016: 368 · 2015: 370 · 2014: 318 · 2013: 274 · 2012: 217
FWCI: 28.35
Type: book
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
Locations: Statistics in the health sciences (publishedVersion)
CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
Keywords: survival analysis, Cox proportional hazards model, semiparametric models, model extension, SAS, S-PLUS

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