An R Shiny App for Sensitivity Analysis for Latent Growth Curve Mediation

Eric S. Kruger, Davood Tofighi, Yu Yu Hsiao, David P. MacKinnon, M. Lee Van Horn, Katie Witkiewitz

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Mechanisms of behavior change are the processes through which interventions are hypothesized to cause changes in outcomes. Latent growth curve mediation models (LGCMM) are recommended for investigating the mechanisms of behavior change because LGCMM models establish temporal precedence of change from the mediator to the outcome variable. The Correlated Augmented Mediation Sensitivity Analyses (CAMSA) App implements sensitivity analysis for LGCMM models to evaluate if a mediating path (mechanism) is robust to potential confounding variables. The CAMSA approach is described and applied to simulated data, and data from a research study exploring a mechanism of change in the treatment of substance use disorder.

Original languageEnglish (US)
Pages (from-to)944-952
Number of pages9
JournalStructural Equation Modeling
Issue number6
StatePublished - 2022


  • Behavior change
  • confounding
  • latent growth curve mediation modeling
  • mediation
  • sensitivity analyses

ASJC Scopus subject areas

  • General Decision Sciences
  • Modeling and Simulation
  • Sociology and Political Science
  • Economics, Econometrics and Finance(all)


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