ArticleStructural equation modeling : a multidisciplinary journal2024
The impact of omitting confounders in parallel process latent growth curve mediation models: Three sensitivity analysis approaches.
Article in Structural equation modeling : a multidisciplinary journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Mediating mechanisms in the discrimination - Mental health link among Mexican-origin adolescents: A latent growth curve mediation analysis.Development and psychopathology · 2026Article
- A Tutorial on Dyadic Growth Curve Mediation Modeling: An Applied Example Testing Pathways of Fathers' Depression Vulnerability to Destructive Marital Conflict.Psychology of men & masculinity · 2025Article
- Causal Estimands and Multiply Robust Estimation of Mediated-Moderation.Multivariate behavioral researchArticle
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Abstract
Parallel process latent growth curve mediation models (PP-LGCMMs) are frequently used to longitudinally investigate the mediation effects of treatment on the level and change of outcome through the level and change of mediator. An important but often violated assumption in empirical PP-LGCMM analysis is the absence of omitted confounders of the relationships among treatment, mediator, and outcome. In this study, we analytically examined how omitting pretreatment confounders impacts the inference of mediation from the PP-LGCMM. Using the analytical results, we developed three sensitivity analysis approaches for the PP-LGCMM, including the frequentist, Bayesian, and Monte Carlo approaches. The three approaches help investigate different questions regarding the robustness of mediation results from the PP-LGCMM, and handle the uncertainty in the sensitivity parameters differently. Applications of the three sensitivity analyses are illustrated using a real-data example. A user-friendly Shiny web application is developed to conduct the sensitivity analyses.
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