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Sequential imputation for models with latent variables assuming latent ignorability.

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Titel: Sequential imputation for models with latent variables assuming latent ignorability.
Autoren: Beesley, Lauren J., Taylor, Jeremy M. G., Little, Roderick J. A.
Quelle: Australian & New Zealand Journal of Statistics; Jun2019, Vol. 61 Issue 2, p213-233, 21p
Schlagwörter: LATENT variables, HEAD & neck cancer, CANCER relapse, DATA distribution
Abstract: Summary: Models 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 called latent ignorable or latent 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 under latent ignorable missingness. The proposed method does 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 model. Imputing missing data for models with latent variables under latent‐dependent missingness without specifying a full joint model [ABSTRACT FROM AUTHOR]
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Datenbank: Complementary Index
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Abstract:Summary: Models 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 called latent ignorable or latent 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 under latent ignorable missingness. The proposed method does 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 model. Imputing missing data for models with latent variables under latent‐dependent missingness without specifying a full joint model [ABSTRACT FROM AUTHOR]
ISSN:13691473
DOI:10.1111/anzs.12264