ArticlePrevention science : the official journal of the Society for Prevention Research2014
An exponential decay model for mediation.
Article in Prevention science : the official journal of the Society for Prevention Research, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed.
- Predicting bilirubin trends in preterm infants using patient specific exponential decay model.Pediatric research · 2025Article
- Article
- Relationship Education and HIV Prevention for Young Male Couples Administered Online via Videoconference: Protocol for a National Randomized Controlled Trial of 2GETHER.JMIR research protocols · 2020Article
- Advancing Analytic Approaches to Address Key Questions in Mechanisms of Behavior Change Research.Journal of studies on alcohol and drugs · 2018Article
- Comparing models of change to estimate the mediated effect in the pretest-posttest control group design.Structural equation modeling : a multidisciplinary journal · 2017Article
- The Application of Intensive Longitudinal Methods to Investigate Change: Stimulating the Field of Applied Family Research.Clinical child and family psychology review · 2016Review
- A Causal Approach to Functional Mediation Analysis with Application to a Smoking Cessation Intervention.Multivariate behavioral researchArticle
- Accuracy of Estimates and Statistical Power for Testing Meditation in Latent Growth Curve Modeling.Structural equation modeling : a multidisciplinary journalArticle
Corrections and comments
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Abstract
Mediation analysis is often used to investigate mechanisms of change in prevention research. Results finding mediation are strengthened when longitudinal data are used because of the need for temporal precedence. Current longitudinal mediation models have focused mainly on linear change, but many variables in prevention change nonlinearly across time. The most common solution to nonlinearity is to add a quadratic term to the linear model, but this can lead to the use of the quadratic function to explain all nonlinearity, regardless of theory and the characteristics of the variables in the model. The current study describes the problems that arise when quadratic functions are used to describe all nonlinearity and how the use of nonlinear functions, such as exponential decay, address many of these problems. In addition, nonlinear models provide several advantages over polynomial models including usefulness of parameters, parsimony, and generalizability. The effects of using nonlinear functions for mediation analysis are then discussed and a nonlinear growth curve model for mediation is presented. An empirical example using data from a randomized intervention study is then provided to illustrate the estimation and interpretation of the model. Implications, limitations, and future directions are also discussed.
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Registered trials
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