The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Standardizing the power of the Hosmer–Lemeshow goodness of fit test in large data sets
Abstract
The Hosmer–Lemeshow test is a commonly used procedure for assessing goodness of fit in logistic regression. It has, for example, been widely used for evaluation of risk‐scoring models. As with any statistical test, the power increases with sample size; this can be undesirable for goodness of fit tests because in very large data sets, small departures from the proposed model will be considered significant. By considering the dependence of power on the number of groups used in the Hosmer–Lemeshow test, we show how the power may be standardized across different sample sizes in a wide range of models. We provide and confirm mathematical derivations through simulation and analysis of data on 31,713 children from the Collaborative Perinatal Project. We make recommendations on how to choose the number of groups in the Hosmer–Lemeshow test based on sample size and provide example applications of the recommendations. Copyright © 2012 John Wiley & Sons, Ltd.
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
Hosmer, D. W., & Lemeshow, S. (2000) · doi:10.1002/0471722146 · Cited by 20,302
Self-citationEuropean system for cardiac operative risk evaluation (EuroSCORE)
Nashef, S. A. M., Roques, F., Michel, P., Gauducheau, E., Lemeshow, S., & Salamon, R. (1999) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/s1010-7940(99)00134-7 · Cited by 3,362
Self-citationThe Sentinel Node in Breast Cancer — A Multicenter Validation Study
Krag, D., Weaver, D., Ashikaga, T., Moffat, F., Klimberg, V. S., Shriver, C., Feldman, S., Kusminsky, R., Gadd, M., Kuhn, J., et al. (1998) · New England Journal of Medicine · doi:10.1056/nejm199810013391401 · Cited by 2,018
Goodness of fit tests for the multiple logistic regression model
Hosmer, D. W., & Lemesbow, S. (1980) · Communications in Statistics - Theory and Methods · doi:10.1080/03610928008827941 · Cited by 1,871
Kramer, A. A., & Zimmerman, J. E. (2007) · Critical Care Medicine · doi:10.1097/01.ccm.0000275267.64078.b0 · Cited by 886
Unified Large-Sample Theory of General Chi-Squared Statistics for Tests of Fit
Moore, D. S., & Spruill, M. C. (1975) · The Annals of Statistics · doi:10.1214/aos/1176343125 · Cited by 204
Newman, T. B., Escobar, G. J., Gonzales, V. M., Armstrong, M. A., Gardner, M. N., & Folck, B. F. (1999) · Pediatrics · doi:10.1542/peds.104.s6.1198 · Cited by 159
Maternal smoking during pregnancy in relation to child overweight: follow-up to age 8 years
Chen, A., Pennell, M. L., Klebanoff, M. A., Rogan, W. J., & Longnecker, M. P. (2005) · International Journal of Epidemiology · doi:10.1093/ije/dyi218 · Cited by 144
Self-citationSome prognostic models for traumatic brain injury were not valid
Hukkelhoven, C. W. P. M., Rampen, A. J. J., Maas, A. I. R., Farace, E., Habbema, J. D. F., Marmarou, A., Marshall, L. F., Murray, G. D., & Steyerberg, E. W. (2006) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2005.06.009 · Cited by 77
The PREM score: a graphical tool for predicting survival in very preterm births
Cole, T. J., Hey, E. N., & Richmond, S. W. J. (2009) · Archives of Disease in Childhood Fetal & Neonatal · doi:10.1136/adc.2009.164533 · Cited by 58
Research works printed but not linked: 3
- 10.1542/peds.104.5.S1.1198 · doi:10.1542/peds.104.5.s1.1198
- 10.2307/2958431 · doi:10.2307/2958431
- 10.2307/2284803 · DOI
Cited by
A tutorial on calibration measurements and calibration models for clinical prediction models
Huang, Y., Li, W., Macheret, F., Gabriel, R. A., & Ohno-Machado, L. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz228 · Cited by 486
Austin, P. C., & Steyerberg, E. W. (2014) · Statistics in Medicine · doi:10.1002/sim.5941 · Cited by 362
Exalto, L. G., Biessels, G. J., Karter, A. J., Huang, E. S., Katon, W. J., Minkoff, J. R., & Whitmer, R. A. (2013) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(13)70048-2 · Cited by 244
Nattino, G., Pennell, M. L., & Lemeshow, S. (2020) · Biometrics · doi:10.1111/biom.13249 · Cited by 229
Self-citationTkacova, R., McNicholas, W. T., Javorsky, M., Fietze, I., Sliwinski, P., Parati, G., Grote, L., & Hedner, J. (2014) · European Respiratory Journal · doi:10.1183/09031936.00225113 · Cited by 156
van Loon, F. H. J., van Hooff, L. W. E., de Boer, H. D., Koopman, S. S. H. A., Buise, M. P., Korsten, H. H. M., Dierick-van Daele, A. T. M., & Bouwman, A. R. A. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8020144 · Cited by 157
Houghton, R., Ong, R. C., & Bolognani, F. (2017) · Autism Research · doi:10.1002/aur.1848 · Cited by 124
Expecting to See a Letter: Alpha Oscillations as Carriers of Top-Down Sensory Predictions
Mayer, A., Schwiedrzik, C. M., Wibral, M., Singer, W., & Melloni, L. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv146 · Cited by 119
Delahanty, R. J., Kaufman, D., & Jones, S. S. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000003011 · Cited by 105
The exposome of healthy and accelerated aging across 40 countries
Hernandez, H., Santamaria-Garcia, H., Moguilner, S., Farina, F. R., Legaz, A., Prado, P., Cuadros, J., Gonzalez, L., Gonzalez-Gomez, R., Migeot, J., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03808-2 · Cited by 71
Chance of live birth: a nationwide, registry-based cohort study
Kolte, A. M., Westergaard, D., Lidegaard, Ø., Brunak, S., & Nielsen, H. S. (2021) · Human Reproduction · doi:10.1093/humrep/deaa326 · Cited by 61
Procedure-based postoperative risk prediction using NSQIP data.
Clark, D. E., Fitzgerald, T. L., & Dibbins, A. W. (2018) · Journal of Surgical Research · doi:10.1016/j.jss.2017.09.003 · Cited by 32
Perceived coercion in persons with mental disorder in India: A cross-sectional study
Raveesh, B., Pathare, S., Lepping, P., Noorthoorn, E., Gowda, G., & Bunders-Aelen, J. G. F. (2016) · Indian Journal of Psychiatry · doi:10.4103/0019-5545.196846 · Cited by 16
Metadata
- Authors
- Paul, P., Pennell, M. L., & Lemeshow, S.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2012-07-26
- Volume
- 32
- Issue
- 1
- Pages
- 67-80
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2166155963
- Cited by
- 432
- References
- 11
- FWCI
- 23.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- onlinelibrary.wiley.com/termsAndConditions#vor (vor)
- doi.wiley.com/10.1002/tdm_license_1.1 (tdm)
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
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.
