Analytic Methods in Project HeartBeat!


      Project HeartBeat! (1991–1995) was an observational study of the development of cardiovascular disease (CVD) risk factors in childhood and adolescence using an accelerated longitudinal design. The purpose of this paper is to explain the analytic methods used in the study, particularly multilevel statistical models. Measurements of hemodynamic, lipid, anthropometric, and other variables were obtained in 678 children who were enrolled in three cohorts (baseline ages 8, 11, and 14 years) and followed for 4 years, resulting in data for children aged 8–18 years. Patterns of change of blood pressure, serum lipid concentration, and obesity with age, race, and gender were of particular interest.
      The design specified 12 measurements of each outcome variable per child. Multilevel models were used to account for correlations resulting from repeated measurements on individuals and to allow use of data from incomplete cases. Data quality–control measures are described, and an example of multilevel analysis in Project HeartBeat! is presented. Multilevel models were also used to show that there were no differences attributable to the cohorts, and combining data from the three age cohorts was judged to be reasonable. Anthropometric data were compared with national norms and shown to have similar patterns; thus, the patterns seen in the CVD risk factors may be generalized, with some caveats, to the U.S. population of children.
      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to American Journal of Preventive Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Labarthe D.R.
        • Nichaman M.Z.
        • Harrist R.B.
        • Grunbaum J.A.
        • Dai S.
        Development of cardiovascular risk factors from ages 8–18 in Project HeartBeat!: study design and patterns of change in plasma total cholesterol concentration.
        Circulation. 1997; 95: 2636-2642
        • Labarthe D.R.
        • Dai S.
        • Day R.S.
        • et al.
        Project HeartBeat!: concept, development, and design.
        Am J Prev Med. 2009; 37: S9-S16
        • Helms R.W.
        Intentionally incomplete longitudinal designs: I.
        Stat Med. 1992; 11: 1889-1913
        • Zeger S.L.
        • Liang K.Y.
        An overview of methods for the analysis of longitudinal data.
        Stat Med. 1992; 11: 1825-1839
        • Rasbash J.
        • Steele F.
        • Browne W.
        • Prosser B.
        A user's guide to MLwiN, version 2.0.
        University of Bristol, 2005
        • Goldstein H.
        Multilevel statistical models.
        3rd ed. Arnold Publishers, London2005
        • Raudenbush S.
        • Bryk A.
        Hierarchical linear models: applications and data analysis methods.
        2nd ed. Sage Publications, Newbury Park CA2002
        • Hox J.
        Multilevel analysis: techniques and applications.
        Earlbaum Associates, Mahwah NJ2002
        • Snijders T.
        • Bosker R.
        Multilevel analysis: an introduction to basic and advanced multilevel modeling.
        Sage Publications, London1999
        • Armstrong N.
        • Welsman J.R.
        • Nevill A.M.
        • Kirby B.J.
        Modeling growth and maturation changes in peak oxygen uptake in 11–13 yr olds.
        J Appl Physiol. 1999; 87: 2230-2236
        • Zvoch K.
        • Stevens J.J.
        Successive student cohorts and longitudinal growth models: an investigation of elementary school mathematics performance.
        Educ Policy Anal Arch. 2006; 14
        • Black M.M.
        • Krishnakumar A.
        Predicting longitudinal growth curves of height and weight using ecological factors for children with and without early growth deficiency.
        J Nutr. 1999; 129: 539S-543S
        • The European Collaborative Study
        Height, weight, and growth in children born to mothers with HIV-1 infection in Europe.
        Pediatrics. 2003; 111: e52-e60
        • Mueller W.H.
        • Harrist R.B.
        • Doyle S.R.
        • Labarthe D.R.
        Percentiles of body composition from bioelectrical impedance and body measurements in U.S. adolescents 8–17 years old: Project HeartBeat!.
        Am J Hum Biol. 2004; 16: 135-150
      1. NHANES I: National Health and Nutrition Examination Survey I (1971–75).
        USDHHS, Public Health Service, CDC, National Center for Health Statistics, Hyattsville MA1981
      2. NHANES II: National Health and Nutrition Examination Survey II (1976–80).
        USDHHS, Public Health Service, CDC, National Center for Health Statistics, Hyattsville MA1984