# Multiple imputation for multilevel data with continuous and binary variables

arXiv: https://arxiv.org/abs/1702.00971 · HTML: https://mlsanity.com/paper/1702.00971/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Feb 2017.

## Built on by (most cited first, 4 of 4)

- [Learning from data with structured missingness](https://mlsanity.com/paper/2304.01429/) (2023)
- [Comparison of Methods that Combine Multiple Randomized Trials to Estimate Heterogeneous Treatment Effects](https://mlsanity.com/paper/2303.16299/) (2023)
- [A Complete Characterisation of Structured Missingness](https://mlsanity.com/paper/2307.02650/) (2023)
- [Adapting tree-based multiple imputation methods for multi-level data? A simulation study](https://mlsanity.com/paper/2401.14161/) (2024)

## Abstract (arXiv)

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.