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A preliminary study of active compared with passive imputation of missing body mass index values among non-Hispanic white youths

¹ From the Department of Human Development and Family Studies, College of Health and Human Development, The Pennsylvania State University, University Park, PA (DAW); the Department of Statistics, University of Connecticut, Storrs, CT (OH); and the Department of Nutrition and Dietetics, East Carolina University, Greenville, NC (SK).

² The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Child Health and Human Development or the National Institutes of Health.

³ Supported in part by grant R03HD050239 from the National Institute of Child Health and Human Development.

⁴ Reprints not available. Address correspondence to DA Wagstaff, S153 Henderson Building, Department of Human Development and Family Studies, College of Health and Human Development, The Pennsylvania State University, University Park, PA 16802. E-mail: daw22{at}psu.edu.

Background: Addressing missing data on body weight, height, or both is a challenge many researchers face. In calculating the body mass index (BMI) of study participants, researchers need to impute the missing data.

Objective: A multiple imputation through a chained equations approach was used to determine whether one should first impute the missing anthropometric data and then calculate BMI or use an imputation model to obtain BMI.

Design: The present study used computer simulation to address the question of how to calculate BMI when there is missing data on weight and height. The simulated data reflected data gathered on non-Hispanic white youths (n = 905) aged 2–18 y, who participated in the 1999–2000 National Health and Nutrition Examination Survey (NHANES).

Results: The simulation indicated that it made little difference in the accuracy with which the youths' mean BMIs were estimated when the data were missing completely at random. However, the use of a model to impute BMI was favored slightly when the data were missing at random and the imputation model included the variable used to determine missingness.

Conclusion: The present findings extend the use of passive imputation and the use of multiple imputation through a chained equations approach to an area of critical public health importance.