The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
The Use of Resampling Methods to Simplify Regression Models in Medical Statistics
Open accessAbstract
SUMMARY: The number of variables in a regression model is often too large and a more parsimonious model may be preferred. Selection strategies (e.g. all-subset selection with various penalties for model complexity, or stepwise procedures) are widely used, but there are few analytical results about their properties. The problems of replication stability, model complexity, selection bias and an over-optimistic estimate of the predictive value of a model are discussed together with several proposals based on resampling methods. The methods are applied to data from a case–control study on atopic dermatitis and a clinical trial to compare two chemotherapy regimes by using a logistic regression and a Cox model. A recent proposal to use shrinkage factors to reduce the bias of parameter estimates caused by model building is extended to parameterwise shrinkage factors and is discussed as a further possibility to illustrate problems of models which are too complex. The results from the resampling approaches favour greater simplicity of the final regression model.
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Cites
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Regression, Prediction and Shrinkage
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A bootstrap resampling procedure for model building: Application to the cox regression model
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Self-citationExploring the Limits of Bootstrap
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Cited by
Variable selection – A review and recommendations for the practicing statistician
Heinze, G., Wallisch, C., & Dunkler, D. (2018) · Biometrical Journal · doi:10.1002/bimj.201700067 · Cited by 1,639
Prognosis Research Strategy (PROGRESS) 3: Prognostic Model Research
Steyerberg, E. W., Moons, K. G. M., van der Windt, D. A., Hayden, J. A., Perel, P., Schroter, S., Riley, R. D., Hemingway, H., Altman, D. G., & for the PROGRESS Group (2013) · PLoS Medicine · doi:10.1371/journal.pmed.1001381 · Cited by 1,508
Prognosis and prognostic research: validating a prognostic model
Altman, D. G., Vergouwe, Y., Royston, P., & Moons, K. G. M. (2009) · BMJ · doi:10.1136/bmj.b605 · Cited by 1,436
Prognosis and prognostic research: Developing a prognostic model
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The use of fractional polynomials to model continuous risk variables in epidemiology
Royston, P., Ambler, G., & Sauerbrei, W. (1999) · International Journal of Epidemiology · doi:10.1093/ije/28.5.964 · Cited by 1,066
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Metadata
- Authors
- Sauerbrei, W.
- Venue
- Journal of the Royal Statistical Society Series C: Applied Statistics
- Publisher
- Oxford University Press (OUP)
- Published
- 1999-09-01
- Volume
- 48
- Issue
- 3
- Pages
- 313-329
- ISSN
- 0035-9254 (ISSN-L)
- 1467-9876
- OpenAlex
- W2073790637
- Cited by
- 310
- References
- 19
- FWCI
- 5.71
- Type
- journal article
- Language
- en
- Open access
- bronze
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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.
