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

David A Wagstaff, Sibylle Kranz, Ofer Harel

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)

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.

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.

Original languageEnglish
Pages (from-to)1025-30
Number of pages6
JournalAmerican Journal of Clinical Nutrition
Volume89
Issue number4
DOIs
Publication statusPublished - Apr 2009

Keywords

  • Adolescent
  • Body Mass Index
  • Child
  • Child, Preschool
  • Computer Simulation
  • Data Collection
  • Data Interpretation, Statistical
  • European Continental Ancestry Group
  • Female
  • Humans
  • Male
  • Outcome Assessment (Health Care)
  • Public Health
  • Reproducibility of Results
  • Research Design

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