A Copula-based Imputation Model for Missing Data of Mixed Type in Multilevel Data Sets

TL;DR

This paper introduces a copula-based latent variable model for imputing missing data of mixed types in multilevel datasets, effectively capturing variable relationships and clustering effects.

Contribution

It presents a novel copula-based imputation method that accounts for multilevel structure and variable types, improving accuracy over traditional methods.

Findings

Achieves good imputation accuracy in simulations

Enhances parameter recovery in clustered data

Adding random effects improves performance with strong clustering

Abstract

We propose a copula based method to handle missing values in multivariate data of mixed types in multilevel data sets. Building upon the extended rank likelihood of \cite{hoff2007extending} and the multinomial probit model, our model is a latent variable model which is able to capture the relationship among variables of different types as well as accounting for the clustering structure. We fit the model by approximating the posterior distribution of the parameters and the missing values through a Gibbs sampling scheme. We use the multiple imputation procedure to incorporate the uncertainty due to missing values in the analysis of the data. Our proposed method is evaluated through simulations to compare it with several conventional methods of handling missing data. We also apply our method to a data set from a cluster randomized controlled trial of a multidisciplinary intervention in acute stroke units. We conclude that our proposed copula based imputation model for mixed type variables achieves reasonably good imputation accuracy and recovery of parameters in some models of interest, and that adding random effects enhances performance when the clustering effect is strong.