ArticleIranian Red Crescent medical journal2011
Multiple imputation in survival models: applied on breast cancer data.
Article in Iranian Red Crescent medical journal, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Loss to follow-up in "test and treat era" and its predictors among HIV-positive adults receiving ART in Northwest Ethiopia: Institution-based cohort study.Frontiers in public health · 2022Article
- Parental physical activity, safety perceptions and children's independent mobility.BMC public health · 2013Article
- Does the missing data imputation method affect the composition and performance of prognostic models?Iranian Red Crescent medical journal · 2012Article
- Prevention of Disease Complications through Diagnostic Models: How to Tackle the Problem of Missing Data?Iranian journal of public health · 2012Article
- Assessment of Internal Validity of Prognostic Models through Bootstrapping and Multiple Imputation of Missing Data.Iranian journal of public health · 2012Article
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Authors and funding
2 authors.
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Abstract
backgroundMissing data is a common problem in cancer research. While simple methods such as completecase (C-C) analysis are commonly employed for handling this problem, several studies have shown that these methods led to biased estimates. We aim to address the methodological issues in development of a prognostic model with missing data.
methodsThree hundred and ten breast cancer patients were enrolled. At first, patients with missing data on any of four candidate variables were omitted. Secondly, missing data were imputed 10 times. Cox regression model was fitted to the C-C and imputed data. Results were compared in terms of variables retained in the model, discrimination ability, and goodness of fit.
resultsSome variables lost their effect in complete-case analysis, due to loss in power, but reached significance level after imputation of missing data. Discrimination ability and goodness of fit of imputed data sets model was higher than that of complete-case model (C-index 76% versus 72%; Likelihood Ratio Test 51.19 versus 32.44).
conclusionOur findings showed inappropriateness of ad hoc complete-case analysis. This approach led to loss in power and imprecise estimates. Application of multiple imputation techniques to avid such problems is recommended.
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