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Estimation of Treatment Policy Estimands for Continuous Outcomes Using Off-Treatment Sequential Multiple Imputation. [PDF]
Pharm StatDrury T, Abellan JJ, Best N, White IR.
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Comparison of common multiple imputation approaches: An application of logistic regression with an interaction. [PDF]
Res Methods Med Health SciSmith MJ +4 more
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The American Statistician, 2005
In the United States, the modern survey sampling revolution began largely at the U.S. Census Bureau. The process, so the story goes, "took off" in the late 1930s when Jerzy Neyman, at the invitation of W. Edwards Deming, came to Washington and lectured at the U.S. Department of Agriculture (USDA 1937; Duncan and Shelton 1978).
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In the United States, the modern survey sampling revolution began largely at the U.S. Census Bureau. The process, so the story goes, "took off" in the late 1930s when Jerzy Neyman, at the invitation of W. Edwards Deming, came to Washington and lectured at the U.S. Department of Agriculture (USDA 1937; Duncan and Shelton 1978).
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Multiple imputation: current perspectives
Statistical Methods in Medical Research, 2007This paper provides an overview of multiple imputation and current perspectives on its use in medical research. We begin with a brief review of the problem of handling missing data in general and place multiple imputation in this context, emphasizing its relevance for longitudinal clinical trials and observational studies with missing covariates.
Kenward, Michael G., Carpenter, James
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Multiple imputation for longitudinal data using Bayesian lasso imputation model
Statistics in Medicine, 2022AbstractMultiple 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 by specifying an imputation model that incorporates auxiliary variables potentially associated with missing ...
Yusuke Yamaguchi +3 more
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Multiple Imputation: An Iterative Regression Imputation
International Journal of Mathematical Sciences and Optimization: Theory and Applications, 2023Multiple imputation (MI) is a commonly applied method of statistically handling missing data. It involves imputing missing values repeatedly to account for the variability due to imputations. There are different techniques of MI that have proven to be effective and available in many statistical software packages.
Bintou, T., Ismaila, A. A.
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2020
In this chapter, we provide an overview over the theory of multiple imputation. Topics covered are justification of multiple imputation strategies based on monotone and non-monotone missing patterns, the approach based on joint modeling of all variables with missing values and the fully conditional modeling approach, where only univariate marginal ...
Kristian Kleinke +3 more
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In this chapter, we provide an overview over the theory of multiple imputation. Topics covered are justification of multiple imputation strategies based on monotone and non-monotone missing patterns, the approach based on joint modeling of all variables with missing values and the fully conditional modeling approach, where only univariate marginal ...
Kristian Kleinke +3 more
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Statistical Methods in Medical Research, 1999
In recent years, multiple imputation has emerged as a convenient and flexible paradigm for analysing data with missing values. Essential features of multiple imputation are reviewed, with answers to frequently asked questions about using the method in practice.
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In recent years, multiple imputation has emerged as a convenient and flexible paradigm for analysing data with missing values. Essential features of multiple imputation are reviewed, with answers to frequently asked questions about using the method in practice.
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Multiple Imputation: Application
2020In this chapter, we discuss the most important and most commonly used multiple imputation tools in R (Table 5.1 gives an overview of the download frequencies of various MI packages in R.) for both multivariate and clustered data sets, including packages mice, norm2, Amelia, mi, pan, as well as function aregImpute( ) from package Hmisc, and show ...
Kristian Kleinke +3 more
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Missing Data and Multiple Imputation
JAMA Pediatrics, 2013Missing data can result in biased estimates of the association between an exposure X and an outcome Y. Even in the absence of bias, missing data can hurt precision, resulting in wider confidence intervals. Analysts should examine the missing data pattern and try to determine the causes of the missingness.
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