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

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.

Keywords:

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

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.

Additional information

Funding

This work was supported by NIAAA R01 [grant number AA025539 to Katie Witkiewitz and Davood Tofighi: MPIs] and NIDA [grant number R37DA09757 to David P. MacKinnon: PI].