title: A preliminary study of active compared with passive imputation of missing body mass index values among non-Hispanic white youths.
abstract: 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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Human Development and Family Studies, College of Health and Human Development, The Pennsylvania State University, University Park, PA 16802, USA. daw22@psu.edu
authors.forename: David A
authors.initials: DA
authors.lastname: Wagstaff
authors.forename: Sibylle
authors.initials: S
authors.lastname: Kranz
authors.forename: Ofer
authors.initials: O
authors.lastname: Harel
mesh_terms: D000293:Adolescent; D015992:Body Mass Index; D002648:Child; D002675:Child, Preschool; D003198:Computer Simulation; D003625:Data Collection / Q000379:methods* / Q000592:standards*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D011634:Public Health; D015203:Reproducibility of Results; D012107:Research Design; D044465:White People / Q000706:statistics & numerical data