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Cited by 233Statistical Methods and Bayesian InferenceStatistical Methods in EpidemiologyAdvanced Causal Inference Techniques

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

A comparison of imputation techniques for handling missing predictor values in a risk model with a binary outcome

Ambler, Gareth, Omar, Rumana Z, Royston, Patrick·2007·Statistical Methods in Medical Research
DOI 10.1177/0962280206074466
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Abstract

Risk models that aim to predict the future course and outcome of disease processes are increasingly used in health research, and it is important that they are accurate and reliable. Most of these risk models are fitted using routinely collected data in hospitals or general practices. Clinical outcomes such as short-term mortality will be near-complete, but many of the predictors may have missing values. A common approach to dealing with this is to perform a complete-case analysis. However, this may lead to overfitted models and biased estimates if entire patient subgroups are excluded. The aim of this paper is to investigate a number of methods for imputing missing data to evaluate their effect on risk model estimation and the reliability of the predictions. Multiple imputation methods, including hotdecking and multiple imputation by chained equations (MICE), were investigated along with several single imputation methods. A large national cardiac surgery database was used to create simulated yet realistic datasets. The results suggest that complete case analysis may produce unreliable risk predictions and should be avoided. Conditional mean imputation performed well in our scenario, but may not be appropriate if using variable selection methods. MICE was amongst the best performing multiple imputation methods with regards to the quality of the predictions. Additionally, it produced the least biased estimates, with good coverage, and hence is recommended for use in practice.

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Research works printed but not linked: 7

  1. Rosen AK, 2003, Insights, 4, 1
  2. Mander A., 2000, Stata Technical Bulletin, 9, 199
  3. Allison PD, 2001, Missing data
  4. Fay RE, Proceedings of the Survey Research Methods Sections
  5. Keogh B., 2002, Systems
  6. Berrisford R., 2005, Surgery, 28, 306
  7. O'Sullivan C., 2005, British Journal of General Practice, 55, 529

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Metadata

Authors
Ambler, G., Omar, R. Z., & Royston, P.
Affiliations
  • UCL Biomedical Research Centre (GB)
  • MRC Clinical Trials Unit at UCL (GB)
Corresponding author
Ambler, Gareth
ORCID
Venue
Statistical Methods in Medical Research
Publisher
SAGE Publications
Published
2007-06
Volume
16
Issue
3
Pages
277-298
ISSN
  • 0962-2802 (ISSN-L)
  • 1477-0334
DOI
10.1177/0962280206074466
OpenAlex
W1965973862
Cited by
233
References
39
Citations by year
2026: 5 · 2025: 8 · 2024: 9 · 2023: 10 · 2022: 6 · 2021: 15 · 2020: 12 · 2019: 13 · 2018: 11 · 2017: 8 · 2016: 11 · 2015: 14 · 2014: 14 · 2013: 12 · 2012: 19
FWCI
15.72
Type
journal article
Language
en
Open access
closed
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Statistical Methods in Medical Research (publishedVersion)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Keywords
missing data imputation, risk prediction model, binary outcomes, multiple imputation, multiple imputation by chained equations, single imputation, complete case analysis, cardiac surgery, mortality prediction, forecast reliability, estimation bias
MeSH terms
Aged, Female, United Kingdom, Humans, Male, Middle Aged, Thoracic Surgery, Models, Statistical, Bias, Risk Assessment

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