Missing Data
Cross-source consensus on Missing Data from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Preparation
Comparisons
Evidence quality
Highlighted claims
- Participants with more than 30% missing data will be excluded before imputation. — Development and real-world cohort validation of a meta-analysis-derived simplified scoring model for cardiac resynchronisation therapy response: a study protocol
- Remaining missing values will be imputed with a K-nearest neighbour algorithm after data normalisation. — Development and real-world cohort validation of a meta-analysis-derived simplified scoring model for cardiac resynchronisation therapy response: a study protocol
- Robustness will be assessed by comparing KNN imputation with multiple imputation by chained equations. — Development and real-world cohort validation of a meta-analysis-derived simplified scoring model for cardiac resynchronisation therapy response: a study protocol
- Gower distance will be used for similarity, and k will be optimized by cross-validation, usually between 5 and 10 neighbours. — Development and real-world cohort validation of a meta-analysis-derived simplified scoring model for cardiac resynchronisation therapy response: a study protocol
- Major discrepancies between KNN and MICE results will be treated as methodological sensitivity. — Development and real-world cohort validation of a meta-analysis-derived simplified scoring model for cardiac resynchronisation therapy response: a study protocol