Multiple imputation for multilevel data with continuous and binary variables

Multiple imputation for multilevel data with continuous and binary variables

Audigier, V.
White, I.R.
Jolani, S.
Debray, T.P.A.
Quartagno, M.
Carpenter, J.
van Buuren, S.
Resche-Rigon,

2017

We present and compare multiple imputation methods for multilevel continuous and binary data where variables are systematically and sporadically missing. The methods are compared from a theoretical point of view and through an extensive simulation study motivated by a real dataset comprising multiple studies. Simulations are reproducible. The comparisons show why these multiple imputation methods are the most appropriate to handle missing values in a multilevel setting and why their relative performances can vary according to the missing data pattern, the multilevel structure and the type of missing variables. This study shows that valid inferences can only be obtained if the dataset gathers a large number of clusters. In addition, it highlights that heteroscedastic MI methods provide more accurate inferences than homoscedastic methods, which should be reserved for data with few individuals per cluster. Finally, the method of Quartagno and Carpenter (2016a) appears generally accurate for binary variables, the method of Resche-Rigon and White (2016) with large clusters, and the approach of Jolani et al. (2015) with small clusters.

Missing data
Multiple imputation
Appended imputation
Nested imputation
Longitudinal data
Congeniality

http://resolver.tudelft.nl/uuid:8a73a3d9-415b-4f0d-947f-a0f73a058bd9

https://doi.org/10.48550/arxiv.1702.00971

981568

ArXiv

article