Sensitivity analysis after multiple imputation under missing at random: a weighting approach
JR Carpenter, MG Kenward… - Statistical methods in …, 2007 - journals.sagepub.com
Multiple imputation (MI) is now well established as a flexible, general, method for the
analysis of data sets with missing values. Most implementations assume the missing data …
analysis of data sets with missing values. Most implementations assume the missing data …
How to handle missing data: A comparison of different approaches
M Peeters, M Zondervan-Zwijnenburg… - European journal of …, 2015 - Taylor & Francis
Many researchers face the problem of missing data in longitudinal research. Especially, high
risk samples are characterized by missing data which can complicate analyses and the …
risk samples are characterized by missing data which can complicate analyses and the …
A simplified framework for using multiple imputation in social work research
Missing data are nearly always a problem in research, and missing values represent a
serious threat to the validity of inferences drawn from findings. Increasingly, social science …
serious threat to the validity of inferences drawn from findings. Increasingly, social science …
Multiple imputation for missing data
PA Patrician - Research in nursing & health, 2002 - Wiley Online Library
Missing data occur frequently in survey and longitudinal research. Incomplete data are
problematic, particularly in the presence of substantial absent information or systematic …
problematic, particularly in the presence of substantial absent information or systematic …
Missing data imputation versus full information maximum likelihood with second-level dependencies
R Larsen - Structural Equation Modeling: A Multidisciplinary …, 2011 - Taylor & Francis
Missing data in the presence of upper level dependencies in multilevel models have never
been thoroughly examined. Whereas first-level subjects are independent over time, the …
been thoroughly examined. Whereas first-level subjects are independent over time, the …
[HTML][HTML] Copy mean: a new method to impute intermittent missing values in longitudinal studies
C Genolini, H Jacqmin-Gadda - Open Journal of Statistics, 2013 - scirp.org
Longitudinal studies are those in which the same variable is repeatedly measured at
different times. These studies are more likely than others to suffer from missing values. Since …
different times. These studies are more likely than others to suffer from missing values. Since …
[图书][B] Multiple imputation and its application
Multiple Imputation and its Application The most up-to-date edition of a bestselling guide to
analyzing partially observed data In this comprehensively revised Second Edition of Multiple …
analyzing partially observed data In this comprehensively revised Second Edition of Multiple …
Guided multiple imputation of missing data: using a subsample to strengthen the missing-at-random assumption
G Fraser, R Yan - Epidemiology, 2007 - journals.lww.com
Multiple imputation can be a good solution to handling missing data if data are missing at
random. However, this assumption is often difficult to verify. We describe an application of …
random. However, this assumption is often difficult to verify. We describe an application of …
Multiple imputation for multivariate missing-data problems: A data analyst's perspective
JL Schafer, MK Olsen - Multivariate behavioral research, 1998 - Taylor & Francis
Analyses of multivariate data are frequently hampered by missing values. Until recently, the
only missing-data methods available to most data analysts have been relatively ad1 hoc …
only missing-data methods available to most data analysts have been relatively ad1 hoc …
Bias and precision of the “multiple imputation, then deletion” method for dealing with missing outcome data
TR Sullivan, AB Salter, P Ryan… - American journal of …, 2015 - academic.oup.com
Multiple imputation (MI) is increasingly being used to handle missing data in epidemiologic
research. When data on both the exposure and the outcome are missing, an alternative to …
research. When data on both the exposure and the outcome are missing, an alternative to …