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Fitting bivariate multilevel models to assess long-term changes in body mass index and cigarette smoking

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  • Folefac D. Atem
  • Ravi K. Sharma
  • Stewart J. Anderson

Abstract

Using data from the National Health interview Survey from 1997 to 2006, we present a multilevel analysis of change in body mass index (BMI) and number of cigarettes smoked per day in the USA. Smoking and obesity are the leading causes of preventable mortality and morbidity in the USA and most parts of the developed world. A two-stage bivariate model of changes in obesity and number of cigarette smoked per day is proposed. At the within subject stage, an individual's BMI status and the number of cigarette smoked per day are jointly modeled as a function of an individual growth trajectory plus a random error. At the between-subject stage, the parameters of the individual growth trajectories are allowed to vary as a function of differences between subjects with respect to demographic and behavioral characteristics and with respect to the four regions of the USA (Northeast, West, South and North central). Our two-stage modeling techniques are more informative than standard regression because they characterize both group-level (nomothetic) and individual-level (idiographic) effects, yielding a more complete understanding of the phenomena under study.

Suggested Citation

  • Folefac D. Atem & Ravi K. Sharma & Stewart J. Anderson, 2011. "Fitting bivariate multilevel models to assess long-term changes in body mass index and cigarette smoking," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(9), pages 1819-1831, September.
  • Handle: RePEc:taf:japsta:v:38:y:2011:i:9:p:1819-1831
    DOI: 10.1080/02664763.2010.529880
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