Much Ado About Nothing
Top Cited Papers
- 1 February 2007
- journal article
- Published by Taylor & Francis Ltd in The American Statistician
- Vol. 61 (1), 79-90
- https://doi.org/10.1198/000313007x172556
Abstract
Missing data are a recurring problem that can cause bias or lead to inefficient analyses. 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 used in practice.Keywords
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