The purpose of this competing continuation grant proposal is to develop, evaluate and apply methodological and statistical procedures to determine how prevention programs change outcome variables. These mediation analyses assess the link between program effects on the constructs targeted by a prevention program and effects on the outcome. As noted by many researchers and federal agencies, mediation analyses identify the most effective program components and increase understanding of the underlying mechanisms leading to changing outcome variables. Information from mediation analysis can make programs more powerful, more efficient, and shorter. The P. I. of this grant received a one-year NIDA small grant and three multi-year grants to develop and evaluate mediation analysis in prevention research. This work led to many publications and innovations. The proposed five-year continuation focuses on the practical use of exciting new mediation analysis statistical developments. Four statistical topics represent next steps in this research and include analytical and simulation research as well as applications to etiological and prevention data. In Study 1, practical issues in the application of recently developed methods, causal mediation and Bayesian mediation analyses, are investigated. Causal mediation and Bayesian methods have the capability to greatly improve the accuracy of mediation analyses. Study 2 investigates solutions to two common problems in prevention research, measurement error and confounder bias, that can make mediation analysis inaccurate. Analyses to correct mediation analysis as well as methods to assess how these problems could affect results will be developed and evaluated. Study 3 develops and evaluates newly developed longitudinal mediation methods based on causal effects and also new methods for data with many repeated measurements. These new methods promise to more accurately model change over time. Study 4 evaluates methods to uncover subgroups in mediation analysis including causal mediation methods and multilevel models for groups and individuals. For each study, we will investigate unique issues with mediation analysis of prevention data including information obtained prior to a study that improve mediation analysis, the timing of change in longitudinal mediation models, and the types and importance of subgroup effects. Study 5 applies new statistical methods to data from several NIH funded prevention data sets providing important feedback about the usefulness of the methods. Study 6 disseminates new information about mediation analysis through our website and other media, by communication with researchers, and publications from the project.
The project investigates a method, statistical mediation analysis that extracts more information from research. Mediation analysis provides information on how and why prevention and treatments are successful. Mediation analysis can improve prevention and treatment so that they have greater effects and even cost less.
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