scholarly journals Explicating the Conditions Under Which Multilevel Multiple Imputation Mitigates Bias Resulting from Random Coefficient-Dependent Missing Longitudinal Data

2016 ◽  
Vol 18 (1) ◽  
pp. 12-19 ◽  
Author(s):  
Nisha C. Gottfredson ◽  
Sonya K. Sterba ◽  
Kristina M. Jackson
Keyword(s):  
Longitudinal Data ◽  
Multiple Imputation ◽  
Random Coefficient ◽  
Multilevel Multiple Imputation

scholarly journals Realignment and multiple imputation of longitudinal data: an application to menstrual cycle data

2011 ◽  
Vol 25 (5) ◽  
pp. 448-459 ◽  
Author(s):  
Sunni L. Mumford ◽  
Enrique F. Schisterman ◽  
Audrey J. Gaskins ◽  
Anna Z. Pollack ◽  
Neil J. Perkins ◽  
...  
Keyword(s):  
Menstrual Cycle ◽  
Longitudinal Data ◽  
Multiple Imputation

A multiple imputation strategy for incomplete longitudinal data

2001 ◽  
Vol 20 (17-18) ◽  
pp. 2741-2760 ◽  
Author(s):  
Mary Beth Landrum ◽  
Mark P. Becker
Keyword(s):  
Longitudinal Data ◽  
Multiple Imputation

Flexible, Free Software for Multilevel Multiple Imputation: A Review of Blimp and jomo

2019 ◽  
Vol 44 (5) ◽  
pp. 625-641
Author(s):  
Timothy Hayes
Keyword(s):  
Multiple Imputation ◽  
Interaction Model ◽  
Missing At Random ◽  
Analysis Model ◽  
Imputation Model ◽  
Model Following ◽  
Software Packages ◽  
Random Intercept ◽  
Impute Data ◽  
Multilevel Multiple Imputation

Multiple imputation is a popular method for addressing data that are presumed to be missing at random. To obtain accurate results, one’s imputation model must be congenial to (appropriate for) one’s intended analysis model. This article reviews and demonstrates two recent software packages, Blimp and jomo, to multiply impute data in a manner congenial with three prototypical multilevel modeling analyses: (1) a random intercept model, (2) a random slope model, and (3) a cross-level interaction model. Following these analysis examples, I review and discuss both software packages.

Multiple Imputation for Longitudinal Data

2021 ◽  
pp. 243-270
Author(s):  
Yulei He ◽  
Guangyu Zhang ◽  
Chiu-Hsieh Hsu
Keyword(s):  
Longitudinal Data ◽  
Multiple Imputation
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