Missing covariate data within cancer prognostic studies: a review of current reporting and proposed guidelines.
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.
Burton
A
A
Cancer Research UK Clinical Trials Unit, University of Birmingham, Birmingham B15 2TT, UK. andrea.burton@cancer.org.uk
Altman
D G
DG
D003657:Decision Making; D017408:Guidelines as Topic; D006801:Humans; D008962:Models, Theoretical; D015999:Multivariate Analysis; D009369:Neoplasms / Q000473:pathology* / Q000628:therapy; D011379:Prognosis; D015203:Reproducibility of Results; D018401:Sample Size