CoChat
Sign Up
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
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1177/0962280206074466

Cites

39 of 39 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Show all 39 references

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

Cited by

Sorted by citations

Items 201–233 of 233

Related

See all

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

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