This article is a U.S. Government work and is in the public domain in the U.S.A.
Multiple imputation of missing dual-energy X-ray absorptiometry data in the National Health and Nutrition Examination Survey†
Article first published online: 30 NOV 2010
This article is a U.S. Government work and is in the public domain in the U.S.A. Published in 2010 by John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 30, Issue 3, pages 260–276, 10 February 2011
How to Cite
Schenker, N., Borrud, L. G., Burt, V. L., Curtin, L. R., Flegal, K. M., Hughes, J., Johnson, C. L., Looker, A. C. and Mirel, L. (2011), Multiple imputation of missing dual-energy X-ray absorptiometry data in the National Health and Nutrition Examination Survey. Statist. Med., 30: 260–276. doi: 10.1002/sim.4080
- Issue published online: 7 JAN 2011
- Article first published online: 30 NOV 2010
- Manuscript Accepted: 4 AUG 2010
- Manuscript Received: 12 AUG 2009
- body composition;
- body fat;
- body mass index;
- bone mineral density;
- missing at random;
- sequential regression multivariate imputation
In 1999, dual-energy x-ray absorptiometry (DXA) scans were added to the National Health and Nutrition Examination Survey (NHANES) to provide information on soft tissue composition and bone mineral content. However, in 1999–2004, DXA data were missing in whole or in part for about 21 per cent of the NHANES participants eligible for the DXA examination; and the missingness is associated with important characteristics such as body mass index and age. To handle this missing-data problem, multiple imputation of the missing DXA data was performed. Several features made the project interesting and challenging statistically, including the relationship between missingness on the DXA measures and the values of other variables; the highly multivariate nature of the variables being imputed; the need to transform the DXA variables during the imputation process; the desire to use a large number of non-DXA predictors, many of which had small amounts of missing data themselves, in the imputation models; the use of lower bounds in the imputation procedure; and relationships between the DXA variables and other variables, which helped both in creating and evaluating the imputations. This paper describes the imputation models, methods, and evaluations for this publicly available data resource and demonstrates properties of the imputations via examples of analyses of the data. The analyses suggest that imputation helps to correct biases that occur in estimates based on the data without imputation, and that it helps to increase the precision of estimates as well. Moreover, multiple imputation usually yields larger estimated standard errors than those obtained with single imputation. Published in 2010 by John Wiley & Sons, Ltd.