Journal ArticleDOI
The use of multiple imputation for the analysis of missing data.
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TLDR
The idea behind MI, the advantages of MI over existing techniques for addressing missing data, how to do MI for real problems, the software available to implement MI, and the results of a simulation study aimed at finding out how assumptions regarding the imputation model affect the parameter estimates provided by MI are discussed.Abstract:
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 missing data point with a set of m > 1 plausible values to generate m complete data sets. These complete data sets are then analyzed by standard statistical software, and the results combined, to give parameter estimates and standard errors that take into account the uncertainty due to the missing data values. This article introduces the idea behind MI, discusses the advantages of MI over existing techniques for addressing missing data, describes how to do MI for real problems, reviews the software available to implement MI, and discusses the results of a simulation study aimed at finding out how assumptions regarding the imputation model affect the parameter estimates provided by MI.read more
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Prognostic Factors and Survival Outcome in Patients with Chordoma in the United States: A Population-Based Analysis.
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Patterns of change in family functioning, resources, coping and parental depression in mothers and fathers of sick newborns over the first year of life
Janet Pinelli,Saroj Saigal,Yow-Wu Bill Wu,Charles E. Cunningham,Alba DiCenso,Susan Steele,Patricia Austin,Steve Turner +7 more
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Cervical cancer screening programmes and age-specific coverage estimates for 202 countries and territories worldwide: a review and synthetic analysis
Laia Bruni,Beatriz Serrano,Esther Roura,Laia Alemany,Melanie J. Cowan,Rolando Herrero,Mario Poljak,Raúl Murillo,Nathalie Broutet,Leanne M. Riley,Silvia de Sanjosé +10 more
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Why Missing Data Matter in the Longitudinal Study of Adolescent Development: Using the 4-H Study to Understand the Uses of Different Missing Data Methods
TL;DR: The results showed that three missing data techniques, i.e., listwise deletion, direct maximum likelihood (DirML), and multiple imputation (MI), did not yield comparable results for research questions assessing different aspects of development (i.e, change over time or prediction effects).
References
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Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
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Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
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TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
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Multiple imputation for nonresponse in surveys
TL;DR: In this article, a survey of drinking behavior among men of retirement age was conducted and the results showed that the majority of the participants reported that they did not receive any benefits from the Social Security Administration.
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Analysis of Incomplete Multivariate Data
TL;DR: The Normal Model Methods for Categorical Data Loglinear Models Methods for Mixed Data and Inference by Data Augmentation Methods for Normal Data provide insights into the construction of categorical and mixed data models.