The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function
Abstract
Summary Typically, regression models for competing risks outcomes are based on proportional hazards models for the crude hazard rates. These estimates often do not agree with impressions drawn from plots of cumulative incidence functions for each level of a risk factor. We present a technique which models the cumulative incidence functions directly. The method is based on the pseudovalues from a jackknife statistic constructed from the cumulative incidence curve. These pseudovalues are used in a generalized estimating equation to obtain estimates of model parameters. We study the properties of this estimator and apply the technique to a study of the effect of alternative donors on relapse for patients given a bone marrow transplant for leukemia.
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Citations
Cites
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A Proportional Hazards Model for the Subdistribution of a Competing Risk
Fine, J. P., & Gray, R. J. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10474144 · Cited by 14,290
The Statistical Analysis of Failure Time Data.
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Survival Analysis: Techniques for Censored and Truncated Data
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Self-citationThe statistical analysis of failure time data
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Szydlo, R., Goldman, J. M., Klein, J. P., Gale, R. P., Ash, R. C., Bach, F. H., Bradley, B. A., Casper, J. T., Flomenberg, N., Gajewski, J. L., et al. (1997) · Journal of Clinical Oncology · doi:10.1200/jco.1997.15.5.1767 · Cited by 468
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Cited by
Investigation of the freely available easy-to-use software ‘EZR’ for medical statistics
Kanda, Y. (2013) · Bone Marrow Transplantation · doi:10.1038/bmt.2012.244 · Cited by 18,767
Risk factors for acute GVHD and survival after hematopoietic cell transplantation
Jagasia, M., Arora, M., Flowers, M. E. D., Chao, N. J., McCarthy, P. L., Cutler, C. S., Urbano-Ispizua, A., Pavletic, S. Z., Haagenson, M. D., Zhang, M.-J., et al. (2012) · Blood · doi:10.1182/blood-2011-06-364265 · Cited by 707
Competing Risk of Death: An Important Consideration in Studies of Older Adults
Berry, S. D., Ngo, L., Samelson, E. J., & Kiel, D. P. (2010) · Journal of the American Geriatrics Society · doi:10.1111/j.1532-5415.2010.02767.x · Cited by 593
Cumulative Incidence in Competing Risks Data and Competing Risks Regression Analysis
Kim, H. T. (2007) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-06-1210 · Cited by 507
Regression modeling of competing risk using R: an in depth guide for clinicians
Scrucca, L., Santucci, A., & Aversa, F. (2010) · Bone Marrow Transplantation · doi:10.1038/bmt.2009.359 · Cited by 458
Metadata
- Authors
- Klein, J. P., & Andersen, P. K.
- Venue
- Biometrics
- Publisher
- Oxford University Press (OUP)
- Published
- 2005-02-28
- Volume
- 61
- Issue
- 1
- Pages
- 223-229
- ISSN
- 0006-341X (ISSN-L)
- 1541-0420
- OpenAlex
- W2165064053
- Cited by
- 349
- References
- 14
- FWCI
- 12.80
- Type
- journal article
- Language
- en
- Open access
- closed
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- Klein, J. P., & Andersen, P. K. (2005). Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function. Biometrics. https://doi.org/10.1111/j.0006-341x.2005.031209.x
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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.
