Sequential imputation for models with latent variables assuming latent ignorability

SummaryModels that involve an outcome variable, covariates, and latent variables are frequently the target for estimation and inference. The presence of missing covariate or outcome data presents a challenge, particularly when missingness depends on the latent variables. This missingness mechanism is calledlatent ignorable orlatent missing at random and is a generalisation of missing at random. Several authors have previously proposed approaches for handling latent ignorable missingness, but these methods rely on prior specification of the joint distribution for the complete data. In practice, specifying the joint distribution can be difficult and/or restrictive. We develop a novel sequential imputation procedure for imputing covariate and outcome data for models with latent variables underlatent ignorable missingness. The proposed methoddoes not require a joint model; rather, we use results under a joint model to inform imputation with less restrictive modelling assumptions. We discuss identifiability and convergence ‐related issues, and simulation results are presented in several modelling settings. The method is motivated and illustrated by a study of head and neck cancer recurrence. Imputing missing data for models with latent variables under latent‐dependent missingness without specifying a full joint mo del.

Source: Australian and New Zealand Journal of Statistics - July 4, 2019 Category: Statistics Authors: Lauren J. Beesley, Jeremy M. G. Taylor, Roderick J. A. Little Tags: Original Article Source Type: research