Critical to the health of the American public are interventions to encourage health promoting behaviors and to discourage health diminishing behaviors. Designing interventions and evaluating their impact over time requires an understanding of the processes by which interventions bring about behavior change. Statistical mediation analysis is used to reveal these processes and to identify the impact of individual components of the intervention that lead to health promoting behaviors and to detect possible counterproductive components that lead to health diminishing behaviors. Statistical mediation analysis of outcomes that emerge over time following an intervention (e.g., sustained exercise and healthy diet, reduced alcohol consumption) pose special challenges from confounding variables that may produce changes in behavior that are not due to the intervention. Separating the effects of these confounding variables on behavior over time is important for assessing true intervention effects. This proposal addresses a class of confounding variables?time-varying confounders (variables over time that are affected by the intervention and affect both the mediator and the outcome). The proposed research addresses two lines of development in statistical mediation; traditional approaches used in prevention science and potential outcomes approaches used in epidemiology and biostatistics, and evaluates their ability to accurately separate confounding influences from longitudinal effects of treatment interventions. The work will improve statistical methods for evaluating long-term treatment effects of health protective interventions. The proposed research training plan will bridge the gap between methods traditional in prevention science and new methods in epidemiology and biostatistics for estimating longitudinal mediated effects in the following aims: 1) Delineate and compare the statistical and causal inference assumptions across traditional methods and new potential outcomes methods for estimating longitudinal mediated effects with time-varying confounding. 2) Conduct a Monte Carlo simulation study to investigate the statistical properties of longitudinal mediated effect estimators of all methods. 3) Apply these methods to extract more information from three NIH funded datasets to compare longitudinal mediated effect estimates on substance use prevention datasets.

Public Health Relevance

Health programs target mediators to reduce substance use and the topic of this research, mediation analysis, reveals how health programs achieve successful effects. The proposed study will integrate, investigate, and apply two different methods to uncover real effects for data measured over several time points. The findings of this study will have significant implications for developing the best ways to assess the impact of health programs on reducing substance use.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Predoctoral Individual National Research Service Award (F31)
Project #
1F31DA043317-01
Application #
9258910
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Jenkins, Richard A
Project Start
2017-05-01
Project End
2019-04-30
Budget Start
2017-05-01
Budget End
2018-04-30
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Arizona State University-Tempe Campus
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
943360412
City
Tempe
State
AZ
Country
United States
Zip Code
85287
MacKinnon, David P; Valente, Matthew J; Wurpts, Ingrid C (2018) Benchmark validation of statistical models: Application to mediation analysis of imagery and memory. Psychol Methods 23:654-671
Mio?evi?, Milica; Gonzalez, Oscar; Valente, Matthew J et al. (2018) A Tutorial in Bayesian Potential Outcomes Mediation Analysis. Struct Equ Modeling 25:121-136
Valente, Matthew J; MacKinnon, David P (2018) SASĀ® Macros for Computing Causal Mediated Effects in Two- and Three-Wave Longitudinal Models. SAS Glob Forum 2018:
Valente, Matthew J; Pelham, William E; Smyth, Heather et al. (2017) Confounding in statistical mediation analysis: What it is and how to address it. J Couns Psychol 64:659-671
Valente, Matthew J; MacKinnon, David P (2017) SASĀ® Macros for Computing the Mediated Effect in the Pretest-Posttest Control Group Design. SAS Glob Forum 2017:
Valente, Matthew J; MacKinnon, David P (2017) Comparing models of change to estimate the mediated effect in the pretest-posttest control group design. Struct Equ Modeling 24:428-450