✦ LIBER ✦

A cautionary case study of approaches to the treatment of missing data

✍ Scribed by Christopher Paul; William M. Mason; Daniel McCaffrey; Sarah A. Fox

Publisher: Springer
Year: 2008
Tongue: English
Weight: 278 KB
Volume: 17
Category: Article
ISSN: 1613-981X
DOI: 10.1007/s10260-007-0090-4

No coin nor oath required. For personal study only.

✦ Synopsis

This article presents findings from a case study of different approachesto the treatment of missing data. Simulations based on data from the Los AngelesMammography Promotion in Churches Program (LAMP) led the authors to the followingcautionary conclusions about the treatment of missing data: (1) Automatedselection of the imputation model in the use of full Bayesian multiple imputation canlead to unexpected bias in coefficients of substantive models. (2) Under conditionsthat occur in actual data, casewise deletion can perform less well than we were led toexpect by the existing literature. (3) Relatively unsophisticated imputations, such as mean imputation and conditional mean imputation, performed better than the technicalliterature led us to expect. (4) To underscore points (1), (2), and (3), the article concludes that imputation models are substantive models, and require the same cautionwith respect to specificity and calculability.

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