A fair comparison of tree-based and parametric methods in multiple imputation by chained equations.

ABSTRACT: Multiple imputation by chained equations (MICE) has emerged as a leading strategy for imputing missing epidemiological data due to its ease of implementation and ability to maintain unbiased effect estimates and valid inference. Within the MICE algorithm, imputation can be performed using a variety of parametric or nonparametric methods. Literature has suggested that nonparametric tree-based imputation methods outperform parametric methods in terms of bias and coverage when there are interactions or other nonlinear effects among the variables. However, these studies fail to provide a fair comparison as they do not follow the well-established recommendation that any effects in the final analysis model (including interactions) should be included in the parametric imputation model. We show vi

SUBMITTER: Slade E 

PROVIDER: S-EPMC9136914 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature