Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines.
Open accessAbstract
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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Commonly missing values are related to observed participant or disease characteristics.
“Commonly missing values are related to observed participant or disease characteristics.”
From Reporting and Methods in Clinical Prediction Research: A Systematic Review (2012) · cites this paper as [57]
Exclusion of participants with missing values often leads to loss of statistical power and biased results.
“Exclusion of participants with missing values will therefore not only lead to loss of statistical power, but often also to biased results 10,12,13,23,24,57,58.”
From Reporting and Methods in Clinical Prediction Research: A Systematic Review (2012) · cites this paper as [57]
Missing values in clinical study data rarely occur completely at random.
“Missing values in clinical study data rarely occur completely at random.”
From Reporting and Methods in Clinical Prediction Research: A Systematic Review (2012) · cites this paper as [57]
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- Authors
- Burton, A., & Altman, D. G.
- Affiliations
- Cancer Research UK (GB)
- Corresponding author
- Burton, A
- ORCID
- Venue
- British Journal of Cancer
- Published
- 2004-06-08
- Volume
- 91
- Issue
- 1
- Pages
- 4-8
- ISSN
- 0007-0920 (ISSN-L)
- 1532-1827
- 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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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.
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