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Modeling Survival Data: Extending the Cox Model
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“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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Metadata
- 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)
- 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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