Accuracy of random-forest-based imputation of missing data in the presence of non-normality, non-linearity, and interaction.

ABSTRACT:

Background

Missing data are common in statistical analyses, and imputation methods based on random forests (RF) are becoming popular for handling missing data especially in biomedical research. Unlike standard imputation approaches, RF-based imputation methods do not assume normality or require specification of parametric models. However, it is still inconclusive how they perform for non-normally distributed data or when there are non-linear relationships or interactions.

Methods

To examine the effects of these three factors, a variety of datasets were simulated with outcome-dependent missing at random (MAR) covariates, and the performances of the RF-based imputation methods missForest and CALIBERrfimpute were evaluated in comparison with predictive mean matching (PMM).

R

SUBMITTER: Hong S 

PROVIDER: S-EPMC7382855 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature