Multiple imputation methods for handling incomplete longitudinal and clustered data where the target analysis is a linear mixed effects model
MH Huque, M Moreno‐Betancur… - Biometrical …, 2020 - Wiley Online Library
Multiple imputation (MI) is increasingly popular for handling multivariate missing data. Two
general approaches are available in standard computer packages: MI based on the …
general approaches are available in standard computer packages: MI based on the …
Impact of the non-distinctness and non-ignorability on the inference by multiple imputation in multivariate multilevel data: a simulation assessment
R Yucel - Journal of Statistical Computation and Simulation, 2017 - Taylor & Francis
Multiple imputation (MI) is an increasingly popular method for analysing incomplete
multivariate data sets. One of the most crucial assumptions of this method relates to …
multivariate data sets. One of the most crucial assumptions of this method relates to …
Population‐calibrated multiple imputation for a binary/categorical covariate in categorical regression models
Multiple imputation (MI) has become popular for analyses with missing data in medical
research. The standard implementation of MI is based on the assumption of data being …
research. The standard implementation of MI is based on the assumption of data being …
[PDF][PDF] Don't Miss Out!: Incomplete data can contain valuable information
I Eekhout - 2015 - research.vumc.nl
In epidemiological research, patient reported outcomes are often measured by a multi-item
questionnaire. In a multi-item questionnaire a construct is measured by combining the …
questionnaire. In a multi-item questionnaire a construct is measured by combining the …
Recovery of information from multiple imputation: a simulation study
Background Multiple imputation is becoming increasingly popular for handling missing data.
However, it is often implemented without adequate consideration of whether it offers any …
However, it is often implemented without adequate consideration of whether it offers any …
[PDF][PDF] Multiple Imputation of Missing Data
J Fox, S Weisberg - Appendix to An R Companion to …, 2018 - socialsciences.mcmaster.ca
After presenting some basic ideas concerning missing data, this appendix explains briefly
how multiple imputation of missing data works, and then illustrates, using the mice package …
how multiple imputation of missing data works, and then illustrates, using the mice package …
The use of multiple imputation for the analysis of missing data.
This article provides a comprehensive review of multiple imputation (MI), a technique for
analyzing data sets with missing values. Formally, MI is the process of replacing each …
analyzing data sets with missing values. Formally, MI is the process of replacing each …
Diagnostic checking of multiple imputation models
Y Zhao - AStA advances in statistical analysis, 2022 - Springer
Abstract Model checking in multiple imputation (MI, Rubin in Multiple imputation for
nonresponse in surveys, Wiley, New York, 1987) becomes increasingly important with the …
nonresponse in surveys, Wiley, New York, 1987) becomes increasingly important with the …
[PDF][PDF] Multiple imputation of missing data in large studies with many variables: A fully conditional specification approach using partial least squares
S Grund, O Lüdtke, A Robitzsch - 2023 - osf.io
Multiple imputation (MI) is one of the most popular methods for handling missing data in
psychological research. However, many imputation approaches are poorly equipped to …
psychological research. However, many imputation approaches are poorly equipped to …