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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-08-31

AUTHORS

Jacques-Emmanuel Galimard, Sylvie Chevret, Emmanuel Curis, Matthieu Resche-Rigon

ABSTRACT

BackgroundMultiple 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 requires joint models for missing observations and their indicators of missingness. In this study, we derived an imputation model for missing binary data with MNAR mechanism from Heckman’s model using a one-step maximum likelihood estimator. We applied this approach to improve a previously developed approach for MNAR continuous outcomes using Heckman’s model and a two-step estimator. These models allow us to use a MICE process and can thus also handle missing at random (MAR) predictors in the same MICE process.MethodsWe simulated 1000 datasets of 500 cases. We generated the following missing data mechanisms on 30% of the outcomes: MAR mechanism, weak MNAR mechanism, and strong MNAR mechanism. We then resimulated the first three cases and added an additional 30% of MAR data on a predictor, resulting in 50% of complete cases. We evaluated and compared the performance of the developed approach to that of a complete case approach and classical Heckman’s model estimates.ResultsWith MNAR outcomes, only methods using Heckman’s model were unbiased, and with a MAR predictor, the developed imputation approach outperformed all the other approaches.ConclusionsIn the presence of MAR predictors, we proposed a simple approach to address MNAR binary or continuous outcomes under a Heckman assumption in a MICE procedure. More... »

PAGES

90

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URI

http://scigraph.springernature.com/pub.10.1186/s12874-018-0547-1

DOI

http://dx.doi.org/10.1186/s12874-018-0547-1

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https://app.dimensions.ai/details/publication/pub.1106471162

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30170561


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