Joint modeling of longitudinal data in multiple behavioral change

Richard Charnigo, Richard Kryscio, Michael T. Bardo, Donald Lynam, Rick S. Zimmerman

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Multiple behavioral change is an exciting and evolving research area, albeit one that presents analytic challenges to investigators. This manuscript considers the problem of modeling jointly trajectories for two or more possibly non-normally distributed dependent variables, such as marijuana smoking and risky sexual activity, collected longitudinally. Of particular scientific interest is applying such modeling to elucidate the nature of the interaction, if any, between an intervention and personal characteristics, such as sensation seeking and impulsivity. The authors describe three analytic approaches: generalized linear mixed modeling, group-based trajectory modeling, and latent growth curve modeling. In particular, the authors identify identify the strengths and weaknesses of these analytic approaches and assess their impact (or lack thereof) on the psychological and behavioral science literature. The authors also compare what investigators have been doing analytically versus what they might want to be doing in the future and discuss the implications for basic and translational research.

Original languageEnglish
Pages (from-to)181-200
Number of pages20
JournalEvaluation and the Health Professions
Issue number2
StatePublished - Jun 2011


  • generalized linear mixed model
  • group-based trajectory model
  • impulsivity
  • joint model
  • latent growth curve model
  • sensation seeking

ASJC Scopus subject areas

  • Health Policy


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