Much Ado About Nothing

By Nicholas J. Horton, K. Kleinman. American Statistician, 2007

479 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/162deb60565a16f00a32dc9b03ebb5f3beb329c0

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Citations in the corpus

A Multilevel Test of Minority Threat Effects on Sentencing (2010, methodology): “In this study, multiple imputation was performed using Patrick Royston’s Imputation by Chained Equations (ICE) program, which is implemented in Stata (see Horton and Kleinman 2007 ).”
Missing covariate data in medical research: to impute is better than to ignore. (2010, unknown): “For a comparison of frequently used software packages for multiple imputation, we refer to the literature [27,28].”
Multiple Imputation of Missing Data: A Simulation Study on a Binary Response (2013, methodology): “Various methods are available, and all impute several values for each missing datum with some random variation, considering that each substitution contains some uncertainty [16].”
Robust factor analysis in the presence of normality violations, missing data, and outliers: Empirical questions and possible solutions (2014, methodology): “There are a number of packages available for performing imputation in R (Horton & Kleinman, 2007).”
Regression with Imputed Covariates: A Generalized Missing Indicator Approach (2011, methodology): “Although frequently used in practice, this approach is known to produce biased estimates (Jones [8], Horton and Kleinman [ 7 ]).”
3D-MICE: integration of cross-sectional and longitudinal imputation for multi-analyte longitudinal clinical data (2017, unknown): “The challenge in imputing time series–based clinical data is that, although numerous imputation algorithms are available, many of these are designed for cross-sectional imputation (measurements at the same time point) and are not well suited to longitudinal clinical data.(20) Clinical data will usua”