Comparing multiple imputation methods for systematically missing subject-level data.
Sign Up to like & getrecommendations! Published in 2017 at "Research synthesis methods"
DOI: 10.1002/jrsm.1192
Abstract: When conducting research synthesis, the collection of studies that will be combined often do not measure the same set of variables, which creates missing data. When the studies to combine are longitudinal, missing data can… read more here.
Keywords: subject level; missing subject; multiple imputation; level ... See more keywords
A fair comparison of tree-based and parametric methods in multiple imputation by chained equations.
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DOI: 10.1002/sim.8468
Abstract: Multiple imputation by chained equations (MICE) has emerged as a leading strategy for imputing missing epidemiological data due to its ease of implementation and ability to maintain unbiased effect estimates and valid inference. Within the… read more here.
Keywords: imputation chained; imputation; multiple imputation; tree based ... See more keywords
Multiple imputation for longitudinal data using Bayesian lasso imputation model
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9315
Abstract: Multiple imputation is a promising approach to handle missing data and is widely used in analysis of longitudinal clinical studies. A key consideration in the implementation of multiple imputation is to obtain accurate imputed values… read more here.
Keywords: imputation model; lasso imputation; multiple imputation; imputation ... See more keywords
Substantive model compatible multilevel multiple imputation: A joint modeling approach
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DOI: 10.1002/sim.9549
Abstract: Substantive model compatible multiple imputation (SMC‐MI) is a relatively novel imputation method that is particularly useful when the analyst's model includes interactions, non‐linearities, and/or partially observed random slope variables. read more here.
Keywords: compatible multilevel; model compatible; model; multiple imputation ... See more keywords
Propensity score matching after multiple imputation when a confounder has missing data
Sign Up to like & getrecommendations! Published in 2023 at "Statistics in Medicine"
DOI: 10.1002/sim.9658
Abstract: One of the main challenges when using observational data for causal inference is the presence of confounding. A classic approach to account for confounding is the use of propensity score techniques that provide consistent estimators… read more here.
Keywords: propensity score; propensity; score matching; multiple imputation ... See more keywords
What difference does multiple imputation make in longitudinal modeling of EQ-5D-5L data? Empirical analyses of simulated and observed missing data patterns
Sign Up to like & getrecommendations! Published in 2021 at "Quality of Life Research"
DOI: 10.1007/s11136-021-03037-3
Abstract: Although multiple imputation is the state-of-the-art method for managing missing data, mixed models without multiple imputation may be equally valid for longitudinal data. Additionally, it is not clear whether missing values in multi-item instruments should… read more here.
Keywords: imputation; multiple imputation; mixed models; approach mixed ... See more keywords
A Bootstrapping Assessment on A U.S. Education Indicator Construction Through Multiple Imputation
Sign Up to like & getrecommendations! Published in 2020 at "Social Indicators Research"
DOI: 10.1007/s11205-020-02507-4
Abstract: Under a matrix sampling design, no students complete all test booklets in the National Assessment of Educational Progress (NAEP). To construct an education indicator on what students know and can do, multiple imputation (MI) is… read more here.
Keywords: imputation; education indicator; multiple imputation; indicator ... See more keywords
The proportion of missing data should not be used to guide decisions on multiple imputation
Sign Up to like & getrecommendations! Published in 2019 at "Journal of Clinical Epidemiology"
DOI: 10.1016/j.jclinepi.2019.02.016
Abstract: Objectives Researchers are concerned whether multiple imputation (MI) or complete case analysis should be used when a large proportion of data are missing. We aimed to provide guidance for drawing conclusions from data with a… read more here.
Keywords: multiple imputation; proportion missing; guide; missing data ... See more keywords
Multiple imputation for nonignorable missing data
Sign Up to like & getrecommendations! Published in 2017 at "Journal of The Korean Statistical Society"
DOI: 10.1016/j.jkss.2017.05.001
Abstract: Multiple imputation is a popular technique for analyzing incomplete data. Missing at random mechanism is often assumed when multiple imputation is performed, assuming that the response mechanism does not depend on the missing variable. However,… read more here.
Keywords: respondents outcome; imputation; response; multiple imputation ... See more keywords
Multiple Imputation of Missing Data in Multilevel Designs: A Comparison of Different Strategies
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DOI: 10.1037/met0000096
Abstract: Multiple imputation is a widely recommended means of addressing the problem of missing data in psychological research. An often-neglected requirement of this approach is that the imputation model used to generate the imputed values must… read more here.
Keywords: imputation; multiple imputation; different strategies; multilevel ... See more keywords
Impact of the non-distinctness and non-ignorability on the inference by multiple imputation in multivariate multilevel data: a simulation assessment
Sign Up to like & getrecommendations! Published in 2017 at "Journal of Statistical Computation and Simulation"
DOI: 10.1080/00949655.2017.1288233
Abstract: ABSTRACT 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 mechanism leading to missing data. Distinctness is typically assumed,… read more here.
Keywords: multiple imputation; non ignorability; simulation; non distinctness ... See more keywords