Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors

Heckman imputation models for binary or continuous MNAR outcomes and MAR predictors

Abstract Background Multiple imputation by chained equations (MICE) requires specifying a suitable conditional imputation model for each incomplete variable and then iteratively imputes the missing values. In the presence of missing not at random (MNAR) outcomes, valid statistical inference often re...

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Bibliographic Details
Main Authors: Jacques-Emmanuel Galimard, Sylvie Chevret, Emmanuel Curis, Matthieu Resche-Rigon
Format: Article
Language:English
Published: BMC 2018-08-01
Series:BMC Medical Research Methodology
Subjects:
Heckman’s model
Missing data
Missing not at random (MNAR)
Multiple imputation by chained equation (MICE)
Sample selection method
Online Access:http://link.springer.com/article/10.1186/s12874-018-0547-1

Internet

http://link.springer.com/article/10.1186/s12874-018-0547-1

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