Multiple imputation for missing data in epidemiological and clinical research¶
Sterne. (2009). Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Imputation Leakage
- Clinical prediction models are the field where this most often inflates published results, because medical datasets are riddled with missing labs and vitals.
This sourceThe finding that missing data are unavoidable in epidemiological and clinical research and are often overlooked, making their handling a routine but error-prone step.
Supported in partVerified against the work's full text
“Missing data are unavoidable in epidemiological and clinical research but their potential to undermine the validity of research results has often been overlooked in the medical literature.”
- Clinical prediction models are the field where this most often inflates published results, because medical datasets are riddled with missing labs and vitals.
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