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 accessAbstract
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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Cites
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BackgroundReview of Software to Fit Generalized Estimating Equation Regression Models
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Cited by
mice: Multivariate Imputation by Chained Equations in R
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Multiple imputation by chained equations: what is it and how does it work?
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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
- OpenAlex
- W2170674956
- Cited by
- 786
- References
- 13
- FWCI
- 34.54
- Type
- journal article
- Language
- en
- Open access
- green
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