Augmented backward elimination: a pragmatic and purposeful way to develop statistical models
Open accessAbstract
Statistical models are simple mathematical rules derived from empirical data describing the association between an outcome and several explanatory variables. In a typical modeling situation statistical analysis often involves a large number of potential explanatory variables and frequently only partial subject-matter knowledge is available. Therefore, selecting the most suitable variables for a model in an objective and practical manner is usually a non-trivial task. We briefly revisit the purposeful variable selection procedure suggested by Hosmer and Lemeshow which combines significance and change-in-estimate criteria for variable selection and critically discuss the change-in-estimate criterion. We show that using a significance-based threshold for the change-in-estimate criterion reduces to a simple significance-based selection of variables, as if the change-in-estimate criterion is not considered at all. Various extensions to the purposeful variable selection procedure are suggested. We propose to use backward elimination augmented with a standardized change-in-estimate criterion on the quantity of interest usually reported and interpreted in a model for variable selection. Augmented backward elimination has been implemented in a SAS macro for linear, logistic and Cox proportional hazards regression. The algorithm and its implementation were evaluated by means of a simulation study. Augmented backward elimination tends to select larger models than backward elimination and approximates the unselected model up to negligible differences in point estimates of the regression coefficients. On average, regression coefficients obtained after applying augmented backward elimination were less biased relative to the coefficients of correctly specified models than after backward elimination. In summary, we propose augmented backward elimination as a reproducible variable selection algorithm that gives the analyst more flexibility in adopting model selection to a specific statistical modeling situation.
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An augmented backward elimination approach was used to select variables for final adjusted models; this approach has been found to reduce bias in variable selection.
“An augmented backward elimination approach, which has been found to reduce bias in variable selection, was used to identify variables for the final adjusted models (Dunkler et al., 2014).”
From Childhood stress and birth timing among African American women: cortisol as biological mediator (2017) · cites this paper as Dunkler et al., 2014
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- Authors
- Dunkler, D., Plischke, M., Leffondré, K., & Heinze, G.
- Affiliations
- Medical University of Vienna (AT)
- Inserm (FR)
- Corresponding author
- Heinze, Georg
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-11-21
- Volume
- 9
- Issue
- 11
- Pages
- e113677
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4240713
- OpenAlex
- W1968850993
- Cited by
- 194
- References
- 39
- Citations by year
- 2026: 16 · 2025: 16 · 2024: 21 · 2023: 23 · 2022: 24 · 2021: 30 · 2020: 25 · 2019: 14 · 2018: 12 · 2017: 1 · 2016: 7 · 2015: 5
- FWCI
- 4.93
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- European Commission (241544)
- Universität Wien
- Medizinische Universität Wien
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- feature selection, backward elimination, statistical model building, regression coefficient bias, SAS macro, logistic regression, Cox proportional hazards model, linear regression
- MeSH terms
- Models, Statistical
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