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Cited by 786

The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.

Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models

Open access
Horton, Nicholas J, Kleinman, Ken P·2007·The American Statistician
DOI 10.1198/000313007x172556
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Abstract

Missing data are a recurring problem that can cause bias or lead to inefficient analyses. Development of statistical methods to address missingness have been actively pursued in recent years, including imputation, likelihood and weighting approaches. Each approach is more complicated when there are many patterns of missing values, or when both categorical and continuous random variables are involved. Implementations of routines to incorporate observations with incomplete variables in regression models are now widely available. We review these routines in the context of a motivating example from a large health services research dataset. While there are still limitations to the current implementations, and additional efforts are required of the analyst, it is feasible to incorporate partially observed values, and these methods should be utilized in practice.

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Metadata

Authors
Horton, N. J., & Kleinman, K. P.
Venue
The American Statistician
Publisher
Informa UK Limited
Published
2007-02
Volume
61
Issue
1
Pages
79-90
ISSN
  • 0003-1305 (ISSN-L)
  • 1537-2731
DOI
10.1198/000313007x172556
OpenAlex
W2170674956
Cited by
786
References
13
FWCI
34.54
Type
journal article
Language
en
Open access
green

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