%0 Journal Article
%@ 0002-9262
%A Shah, AD
%A Bartlett, JW
%A Carpenter, J
%A Nicholas, O
%A Hemingway, H
%D 2014
%F discovery:1427772
%J AMERICAN JOURNAL OF EPIDEMIOLOGY
%K angina, stable, imputation, missing data, missingness at random, regression trees, simulation, survival
%N 6
%P 764 - 774
%T Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study
%U https://discovery.ucl.ac.uk/id/eprint/1427772/
%V 179
%X Multivariate imputation by chained equations (MICE) is commonly used for imputing missing data in epidemiologic  research. The “true” imputation model may contain nonlinearities which are not included in default imputation  models. Random forest imputation is a machine learning technique which can accommodate nonlinearities and interactions  and does not require a particular regression model to be specified.We compared parametric MICE with a  random forest-based MICE algorithm in 2 simulation studies. The first study used 1,000 random samples of 2,000  persons drawn from the 10,128 stable angina patients in the CALIBER database (Cardiovascular Disease Research  using Linked Bespoke Studies and Electronic Records; 2001–2010) with complete data on all covariates.  Variables were artificially made “missing at random,” and the bias and efficiency of parameter estimates obtained  using different imputation methods were compared. Both MICE methods produced unbiased estimates of (log) hazard  ratios, but random forest was more efficient and produced narrower confidence intervals. The second study  used simulated data in which the partially observed variable depended on the fully observed variables in a nonlinear  way. Parameter estimates were less biased using random forest MICE, and confidence interval coverage was better.  This suggests that random forest imputation may be useful for imputing complex epidemiologic data sets in  which some patients have missing data.
%Z © The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.  This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License  (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted reuse, distribution, and reproduction in any medium,  provided the original work is properly cited.    PubMed ID: 24589914