Abstract
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.
Disclosure Statement
The authors do not have any conflicts of interest.
Data Availability
Data (except for raw substantive data) and app code are available at https://osf.io/awzd2/
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.
Notes
1 Tofighi et al. (362019) showed that confounder correlations are a function of the effects of a hypothesized omitted confounder on the endogenous variables. They showed equivalence between a LGCMM with correlated residuals and a LGCMM with a hypothesized omitted confounder.