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<https://discovery.ucl.ac.uk/id/eprint/1427772> <http://purl.org/dc/terms/title> "Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study"^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/1427772> <http://purl.org/ontology/bibo/abstract> "Multivariate imputation by chained equations (MICE) is commonly used for imputing missing data in epidemiologic\r\nresearch. The “true” imputation model may contain nonlinearities which are not included in default imputation\r\nmodels. Random forest imputation is a machine learning technique which can accommodate nonlinearities and interactions\r\nand does not require a particular regression model to be specified.We compared parametric MICE with a\r\nrandom forest-based MICE algorithm in 2 simulation studies. The first study used 1,000 random samples of 2,000\r\npersons drawn from the 10,128 stable angina patients in the CALIBER database (Cardiovascular Disease Research\r\nusing Linked Bespoke Studies and Electronic Records; 2001–2010) with complete data on all covariates.\r\nVariables were artificially made “missing at random,” and the bias and efficiency of parameter estimates obtained\r\nusing different imputation methods were compared. Both MICE methods produced unbiased estimates of (log) hazard\r\nratios, but random forest was more efficient and produced narrower confidence intervals. The second study\r\nused simulated data in which the partially observed variable depended on the fully observed variables in a nonlinear\r\nway. Parameter estimates were less biased using random forest MICE, and confidence interval coverage was better.\r\nThis suggests that random forest imputation may be useful for imputing complex epidemiologic data sets in\r\nwhich some patients have missing data."^^<http://www.w3.org/2001/XMLSchema#string> .
<https://discovery.ucl.ac.uk/id/eprint/1427772> <http://purl.org/dc/terms/date> "2014-01-12" .
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