The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Penalized Survival Models and Frailty
Abstract
Interest in the use of random effects in the survival analysis setting has been increasing. However, the computational complexity of such frailty models has limited their general use. Although fitting frailty models has traditionally been difficult, standard algorithms for fitting Cox semiparametric and parametric regression models can be readily extended to include penalized regression. We demonstrate that solutions for gamma shared frailty models can be obtained exactly via penalized estimation. Similarly, Gaussian frailty models are closely linked to penalized models. Fitting frailty models with penalized likelihoods can be made quite efficient by taking advantage of computational methods available for penalized models. We have implemented penalized regression for the coxph function of S-Plus and illustrate the algorithms with examples using the Cox model.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Approximate Inference in Generalized Linear Mixed Models
Breslow, N. E., & Clayton, D. G. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10594284 · Cited by 4,629
Hurvich, C. M., Simonoff, J. S., & Tsai, C.-L. (1998) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00125 · Cited by 1,241
Scandinavian Journal of Statistics
(2010) · Scandinavian Journal of Statistics · doi:10.1111/(issn)1467-9469 · Cited by 976
Cox regression analysis of multivariate failure time data: The marginal approach
Lin, D. Y. (1994) · Statistics in Medicine · doi:10.1002/sim.4780132105 · Cited by 588
A counting process approach to maximum likelihood estimation in frailty models
Nielsen, G. G., Gill, R. D., Andersen, P. K., & Sørensen, T. I. A. (1992) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 538
Regression with Frailty in Survival Analysis
McGilchrist, C. A., & Aisbett, C. W. (1991) · Biometrics · doi:10.2307/2532138 · Cited by 526
Gray, R. J. (1992) · Journal of the American Statistical Association · doi:10.1080/01621459.1992.10476248 · Cited by 502
Ursodeoxycholic acid in the treatment of primary biliary cirrhosis
Lindor, K. D., Dickson, E. R., Baldus, W. P., Jorgensen, R. A., Ludwig, J., Murtaugh, P. A., Harrison, J. M., Wiesner, R. H., Anderson, M. L., Lange, S. M., et al. (1994) · Gastroenterology · doi:10.1016/0016-5085(94)90021-3 · Cited by 487
Semiparametric Estimation of Random Effects Using the Cox Model Based on the EM Algorithm
Klein, J. P. (1992) · Biometrics · doi:10.2307/2532345 · Cited by 435
Estimation of Multivariate Frailty Models Using Penalized Partial Likelihood
Ripatti, S., & Palmgren, J. (2000) · Biometrics · doi:10.1111/j.0006-341x.2000.01016.x · Cited by 398
Cited by
Genomic classification and prognosis in acute myeloid leukemia
Papaemmanuil, E., Gerstung, M., Bullinger, L., Gaidzik, V. I., Paschka, P., Roberts, N. D., Potter, N. E., Heuser, M., Thol, F., Bolli, N., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1516192 · Cited by 4,350
Incidence and Long-Term Outcomes of Hypertensive Disorders of Pregnancy
Garovic, V. D., White, W. M., Vaughan, L., Saiki, M., Parashuram, S., Garcia-Valencia, O., Weissgerber, T. L., Milic, N., Weaver, A., & Mielke, M. M. (2020) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.03.028 · Cited by 426
Metastatic recurrence in colorectal cancer arises from residual EMP1+ cells
Cañellas-Socias, A., Cortina, C., Hernando-Momblona, X., Palomo-Ponce, S., Mulholland, E. J., Turon, G., Mateo, L., Conti, S., Roman, O., Sevillano, M., et al. (2022) · Nature · doi:10.1038/s41586-022-05402-9 · Cited by 256
McCoy, T. H., Castro, V. M., Roberson, A. M., Snapper, L. A., & Perlis, R. H. (2016) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2016.2172 · Cited by 130
Waugh, K. A., Minter, R., Baxter, J., Chi, C., Galbraith, M. D., Tuttle, K. D., Eduthan, N. P., Kinning, K. T., Andrysik, Z., Araya, P., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01399-7 · Cited by 117
Metadata
- Authors
- Therneau, T. M., Grambsch, P. M., & Pankratz, V. S.
- Venue
- Journal of Computational and Graphical Statistics
- Publisher
- Informa UK Limited
- Published
- 2003-03
- Volume
- 12
- Issue
- 1
- Pages
- 156-175
- ISSN
- 1061-8600 (ISSN-L)
- 1537-2715
- OpenAlex
- W1985420066
- Cited by
- 481
- References
- 16
- FWCI
- 9.45
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Penalized Survival Models and Frailty. https://app.cochat.ai/papers/10.1198/1061860031365
- The paper
- Therneau, T. M., Grambsch, P. M., & Pankratz, V. S. (2003). Penalized Survival Models and Frailty. Journal of Computational and Graphical Statistics. https://doi.org/10.1198/1061860031365
Data sources
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.
