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The identifiability of the competing risks model
Abstract
This paper considers the consequences for identifiability of introducing regressors into the competing risks model of multistate duration analysis. We establish conditions under which access to regressors overturns the nonidentification theorem of Cox and Tsiatis for both proportional and accelerated failure time models.
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Citations
Cited by
Copula Modeling: An Introduction for Practitioners
Trivedl, P. K., & Zimmer, D. M. (2007) · Foundations and Trends in Econometrics · doi:10.1561/0800000005 · Cited by 867
The Nonparametric Identification of Treatment Effects in Duration Models
Abbring, J. H., & van den Berg, G. J. (2003) · Econometrica · doi:10.1111/1468-0262.00456 · Cited by 616
Frailty Models in Survival Analysis
Wienke, A. (2010) · Chapman & Hall/CRC eBooks · doi:10.1201/9781420073911 · Cited by 454
Efficient Estimation of Semiparametric Multivariate Copula Models
Chen, X., Fan, Y., & Tsyrennikov, V. (2006) · Journal of the American Statistical Association · doi:10.1198/016214506000000311 · Cited by 256
Abbring, J. H., & Heckman, J. J. (2007) · Handbook of Econometrics · doi:10.1016/s1573-4412(07)06072-2 · Cited by 122
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Metadata
- Authors
- HECKMAN, J. J., & HONORÉ, B. E.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1989
- Volume
- 76
- Issue
- 2
- Pages
- 325-330
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2066380011
- Cited by
- 316
- References
- 0
- FWCI
- 3.68
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: The identifiability of the competing risks model. https://app.cochat.ai/papers/10.1093/biomet/76.2.325
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- HECKMAN, J. J., & HONORÉ, B. E. (1989). The identifiability of the competing risks model. Biometrika. https://doi.org/10.1093/biomet/76.2.325
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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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
