Cited by 219Statistical Methods in EpidemiologyStatistical Methods and Bayesian InferenceMeta-analysis and systematic reviews

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Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines.

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Burton, A, Altman, D G·2004·British Journal of Cancer

DOI 10.1038/sj.bjc.6601907PMC PMC2364743

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Abstract

Prognostic models play a crucial role in the clinical decision-making process. Unfortunately, missing covariate data impede the construction of valid and reliable models, potentially introducing bias, if handled inappropriately. The extent of missing covariate data within reported cancer prognostic studies, the current handling and the quality of reporting this missing covariate data are unknown. Therefore, a review was conducted of 100 articles reporting multivariate survival analyses to assess potential prognostic factors, published within seven cancer journals in 2002. Missing covariate data is a common occurrence in studies performing multivariate survival analyses, being apparent in 81 of the 100 articles reviewed. The percentage of eligible cases with complete data was obtainable in 39 articles, and was <90% in 17 of these articles. The methods used to handle incomplete covariates were obtainable in 32 of the 81 articles with known missing data and the most commonly reported approaches were complete case and available case analysis. This review has highlighted deficiencies in the reporting of missing covariate data. Guidelines for presenting prognostic studies with missing covariate data are proposed, which if followed should clarify and standardise the reporting in future articles.

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Authors: Burton, A., & Altman, D. G.
Affiliations: Cancer Research UK (GB)
Corresponding author: Burton, A
ORCID: Burton, A
Altman, D G
Venue: British Journal of Cancer
Publisher: Springer Science and Business Media LLC
Published: 2004-06-08
Volume: 91
Issue: 1
Pages: 4-8
ISSN: 0007-0920 (ISSN-L)
1532-1827
DOI: 10.1038/sj.bjc.6601907
PMC: PMC2364743
OpenAlex: W2040958097
Cited by: 219
References: 18
Citations by year: 2026: 3 · 2025: 9 · 2024: 9 · 2023: 11 · 2022: 10 · 2021: 7 · 2020: 12 · 2019: 11 · 2018: 13 · 2017: 13 · 2016: 7 · 2015: 9 · 2014: 16 · 2013: 7 · 2012: 20
FWCI: 8.78
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-sa
www.springer.com/tdm (unspecified)
Locations: British Journal of Cancer (publishedVersion, cc-by-nc-sa)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
Funders: Cancer Research UK
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords: missing covariates, reporting quality, multivariate survival analysis, complete case analysis, available case analysis, missing data handling, reporting guidelines, prognostic factors
MeSH terms: Decision Making, Humans, Models, Theoretical, Neoplasms, Prognosis, Reproducibility of Results, Multivariate Analysis, Guidelines as Topic, Sample Size

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Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines.

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