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Multiple imputation as a missing data machine.

Authors: Brand, J.; Buuren, S. van; Mulligen, E.M. van; Timmers, T.; Gelsema, E.;

Multiple imputation as a missing data machine.

Abstract

This paper deals with problems concerning missing data in clinical databases. After signalling some shortcomings of popular solutions to incomplete data problems, we outline the concepts behind multiple imputation. Multiple imputation is a statistically sound method for handling incomplete data. Application of multiple imputation requires a lot of work and not every user is able to do this. A transparent implementation of multiple imputation is necessary. Such an implementation is possible in the HERMES medical workstation. A remaining problem is to find proper imputations.

Country
Netherlands
Related Organizations
Keywords

Computer Systems, Data Interpretation, Statistical, Database Management Systems, Healthy Living, Mathematical Computing, Algorithms

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    influence
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Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Top 10%
Average
Related to Research communities
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