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.
Keywords
Missing dataCategorical variableImputation (statistics)Computer scienceImplementationWeightingContext (archaeology)RegressionInverse probability weightingData miningData scienceMachine learningStatisticsMathematics
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Publication Info
- Year
- 2007
- Type
- article
- Volume
- 61
- Issue
- 1
- Pages
- 79-90
- Citations
- 759
- Access
- Closed
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Cite This
Nicholas J. Horton,
Ken Kleinman
(2007).
Much Ado About Nothing.
The American Statistician
, 61
(1)
, 79-90.
https://doi.org/10.1198/000313007x172556
Identifiers
- DOI
- 10.1198/000313007x172556