Multiple imputation and functional methods in the presence of measurement error and missingness in explanatory variables

Noghrehchi, Firouzeh; Stoklosa, Jakub; Penev, Spiridon

doi:10.1007/s00180-020-00976-2

Cited by 4 publications

(3 citation statements)

References 39 publications

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“…Noghrehchi et al. (2020) combined multiple imputation with functional methods for error correction in a joint framework, and most recently van Smeden et al. (2021) compared four different methods that address both ME and missing data, among them a Bayesian model.…”

Section: Introductionmentioning

confidence: 99%

“…Soon after, Goldstein et al (2018) described a hierarchical Bayesian model with a classical ME and imputation model that allows the covariate with missing values to depend on other variables, and Keogh and Bartlett (2021) considered ME as a missing data problem by demonstrating multiple ways to utilize this link. Noghrehchi et al (2020) combined multiple imputation with functional methods for error correction in a joint framework, and most recently van Smeden et al (2021) compared four different methods that address both ME and missing data, among them a Bayesian model. The integrated nested Laplace approximation framework enables Bayesian inference for complex hierarchical models and has over the past decade become a popular tool for a wide variety of statistical models (Rue et al, 2009;Martino & Riebler, 2020;Gómez-Rubio, 2020).…”

Section: Introductionmentioning

confidence: 99%

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A joint Bayesian framework for missing data and measurement error using integrated nested Laplace approximations

Skarstein,

Martino,

Muff

2023

Biometrical J

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Measurement error (ME) and missing values in covariates are often unavoidable in disciplines that deal with data, and both problems have separately received considerable attention during the past decades. However, while most researchers are familiar with methods for treating missing data, accounting for ME in covariates of regression models is less common. In addition, ME and missing data are typically treated as two separate problems, despite practical and theoretical similarities. Here, we exploit the fact that missing data in a continuous covariate is an extreme case of classical ME, allowing us to use existing methodology that accounts for ME via a Bayesian framework that employs integrated nested Laplace approximations (INLA) and thus to simultaneously account for both ME and missing data in the same covariate. As a useful by‐product, we present an approach to handle missing data in INLA since this corresponds to the special case when no ME is present. In addition, we show how to account for Berkson ME in the same framework. In its broadest generality, the proposed joint Bayesian framework can thus account for Berkson ME, classical ME, and missing data, or any combination of these in the same or different continuous covariates of the family of regression models that are feasible with INLA. The approach is exemplified using both simulated and real data. We provide extensive and fully reproducible Supporting Information with thoroughly documented examples using R‐INLA and inlabru.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A joint Bayesian framework for missing data and measurement error using integrated nested Laplace approximations

Skarstein,

Martino,

Muff

2023

Biometrical J

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“…So he suggested creating multiple imputations that could reflect the uncertainty of the missing data. Then, the complete datasets resultant can be analyzed separately, and its results combined together (Noghrehchi et al, 2020). Among the different methods of imputing missing values the archetypes can be used to perform single or multiple imputation.…”

Section: Imputation Methodsmentioning

confidence: 99%

Archetypal analysis as an imputation method and multivariate data augmentation

Cavalcanti¹

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Two-step semiparametric empirical likelihood inference from capture–recapture data with missing covariates

Liu,

et al. 2024

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Multiple imputation and functional methods in the presence of measurement error and missingness in explanatory variables

Cited by 4 publications

References 39 publications

A joint Bayesian framework for missing data and measurement error using integrated nested Laplace approximations

A joint Bayesian framework for missing data and measurement error using integrated nested Laplace approximations

Archetypal analysis as an imputation method and multivariate data augmentation

Two-step semiparametric empirical likelihood inference from capture–recapture data with missing covariates

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