The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Assessing time‐by‐covariate interactions in proportional hazards regression models using cubic spline functions
Abstract
Proportional hazards (or Cox) regression is a popular method for modelling the effects of prognostic factors on survival. Use of cubic spline functions to model time‐by‐covariate interactions in Cox regression allows investigation of the shape of a possible covariate‐time dependence without having to specify a specific functional form. Cubic spline functions allow one to graph such time‐by‐covariate interactions, to test formally for the proportional hazards assumption, and also to test for non‐linearity of the time‐by‐covariate interaction. The functions can be fitted with existing software using relatively few parameters; the regression coefficients are estimated using standard maximum likelihood methods.
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Citations
Cites
Partial residuals for the proportional hazards regression model
SCHOENFELD, D. (1982) · Biometrika · doi:10.1093/biomet/69.1.239 · Cited by 3,111
Cited by
Flexible Parametric Alternatives to the Cox Model, and more
Royston, P. (2001) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x0100100101 · Cited by 259
Kang, X., Zhang, Q., Wang, S., Huang, X., & Jin, S. (2010) · Canadian Medical Association Journal · doi:10.1503/cmaj.091298 · Cited by 141
Metadata
- Authors
- Hess, K. R.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 1994-05-30
- Volume
- 13
- Issue
- 10
- Pages
- 1045-1062
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2097239572
- Cited by
- 212
- References
- 1
- FWCI
- 2.49
- Type
- journal article
- Language
- en
- Open access
- closed
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- Hess, K. R. (1994). Assessing time‐by‐covariate interactions in proportional hazards regression models using cubic spline functions. Statistics in Medicine. https://doi.org/10.1002/sim.4780131007
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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