Multiple Imputation for Missing Body-Scan (DXA) Data in the National Health and Nutrition Examination Survey

• Activity Number: 440
• Type: Invited
• Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Section on Survey Research Methods
• Author(s): Nathaniel Schenker*+ and Lori G Borrud and Vicki L. Burt and Lester R. Curtin and Katherine M. Flegal and Jeffery Hughes and Clifford L. Johnson and Anne C. Looker and Lisa Mirel
• Companies: National Center for Health Statistics and National Center for Health Statistics and National Center for Health Statistics and National Center for Health Statistics and National Center for Health Statistics and National Center for Health Statistics and National Center for Health Statistics and National Center for Health Statistics and National Center for Health Statistics
• Keywords: body composition ; body fat ; body mass index ; bone mineral density ; missing at random ; SRMI

Abstract

In 1999, dual-energy x-ray absorptiometry (DXA) scans were added to the National Health and Nutrition Examination Survey to provide important information on body composition. However, in 1999-2004, DXA data were missing at least partially for about 21 percent of the eligible participants; and the missingness was associated with important characteristics such as body mass index and age. Multiple imputation of the missing DXA data was performed. Features making the project interesting and challenging statistically included the relationship between DXA missingness 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 having small amounts of missing data, 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 talk describes the imputation models, methods, and evaluations, and demonstrates properties of the imputations via examples of analyses of the data.