The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Outcome modelling strategies in epidemiology: traditional methods and basic alternatives
Open accessAbstract
Controlling for too many potential confounders can lead to or aggravate problems of data sparsity or multicollinearity, particularly when the number of covariates is large in relation to the study size. As a result, methods to reduce the number of modelled covariates are often deployed. We review several traditional modelling strategies, including stepwise regression and the 'change-in-estimate' (CIE) approach to deciding which potential confounders to include in an outcome-regression model for estimating effects of a targeted exposure. We discuss their shortcomings, and then provide some basic alternatives and refinements that do not require special macros or programming. Throughout, we assume the main goal is to derive the most accurate effect estimates obtainable from the data and commercial software. Allowing that most users must stay within standard software packages, this goal can be roughly approximated using basic methods to assess, and thereby minimize, mean squared error (MSE).
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
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Causality: Models, Reasoning, and Inference
Pearl, J. (2009) · doi:10.1017/cbo9780511803161 · Cited by 11,653
Harrell, F. E. (2001) · Springer Series in Statistics · doi:10.1007/978-1-4757-3462-1 · Cited by 9,195
Bootstrap Methods and their Application
Davison, A. C., & Hinkley, D. V. (1997) · doi:10.1017/cbo9780511802843 · Cited by 7,350
Sontrop, J. M., Dixon, S. N., Garg, A. X., Buendia-Jimenez, I., Dohein, O., Huang, S.-H. S., & Clark, W. F. (2013) · American Journal of Nephrology · doi:10.1159/000350377 · Cited by 5,871
Bootstrap Methods and Their Application
Loughin, T. M., Davison, A. C., & Hinkley, D. V. (1999) · Journal of the American Statistical Association · doi:10.2307/2669707 · Cited by 5,418
Bias reduction of maximum likelihood estimates
FIRTH, D. (1993) · Biometrika · doi:10.1093/biomet/80.1.27 · Cited by 5,035
Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating
Steyerberg, E. W. (2019) · Statistics for Biology and Health · doi:10.1007/978-3-030-16399-0 · Cited by 3,281
Simulation Study of Confounder-Selection Strategies
Maldonado, G., & Greenland, S. (1993) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a116813 · Cited by 2,491
Self-citationTHE IMPACT OF CONFOUNDER SELECTION CRITERIA ON EFFECT ESTIMATION
MICKEY, R. M., & GREENLAND, S. (1989) · American Journal of Epidemiology · doi:10.1093/oxfordjournals.aje.a115101 · Cited by 2,362
Self-citation
Cited by
Mendonça, R. D. D., Lopes, A. C. S., Pimenta, A. M., Gea, A., Martinez-Gonzalez, M. A., & Bes-Rastrollo, M. (2017) · American Journal of Hypertension · doi:10.1093/ajh/hpw137 · Cited by 488
Causal Inference in Environmental Epidemiology: Old and New Approaches
Pearce, N., Vandenbroucke, J. P., & Lawlor, D. A. (2019) · Epidemiology · doi:10.1097/ede.0000000000000987 · Cited by 75
Self-citationO'Callaghan-Gordo, C., Kogevinas, M., Cirach, M., Castaño-Vinyals, G., Aragonés, N., Delfrade, J., Fernández-Villa, T., Amiano, P., Dierssen-Sotos, T., Tardon, A., et al. (2018) · International Journal of Hygiene and Environmental Health · doi:10.1016/j.ijheh.2018.07.014 · Cited by 63
Fernandes, M. D. S. V., Silva, T. M. V. D., Noll, P. R. E. S., Almeida, A. A. D., & Noll, M. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19063735 · Cited by 21
England-Mason, G., Merrill, S. M., Liu, J., Martin, J. W., MacDonald, A. M., Kinniburgh, D. W., Gladish, N., MacIsaac, J. L., Giesbrecht, G. F., Letourneau, N., et al. (2024) · Epigenomes · doi:10.3390/epigenomes8030031 · Cited by 10
Metadata
- Authors
- Greenland, S., Daniel, R., & Pearce, N.
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2016-04-01
- Volume
- 45
- Issue
- 2
- Pages
- 565-575
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- OpenAlex
- W2340199516
- Cited by
- 293
- References
- 61
- FWCI
- 17.20
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: Outcome modelling strategies in epidemiology: traditional methods and basic alternatives. https://app.cochat.ai/papers/10.1093/ije/dyw040
- The paper
- Greenland, S., Daniel, R., & Pearce, N. (2016). Outcome modelling strategies in epidemiology: traditional methods and basic alternatives. International Journal of Epidemiology. https://doi.org/10.1093/ije/dyw040
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.
