NIH Methods Series Webinar

Modern meta-analytic methods for prevention science

The purpose of this webinar is to expand prevention scientists’ meta-analytic toolkit by providing an overview of three modern meta-analytic techniques. Dr. Emily Tanner-Smith describes meta-analytic structural equation modeling approaches and their utility for the development and testing of theoretical models and causal pathways. She discusses network meta-analysis and component network meta-analysis techniques that can be used to address questions about the comparative effectiveness of different prevention programs and active program ingredients. Finally, she discusses robust variance estimation approaches that can be used to address multiplicity in effect size estimates, which commonly occur in syntheses of the prevention literature.

Additional Webinar Trainings

Tutorials

Robust variance estimation with dependent effect sizes: Practical considerations including a software tutorial in Stata and SPSS

Methodologists have recently proposed robust variance estimation as one way to handle dependent effect sizes in meta-analysis. Software macros for robust variance estimation in meta-analysis are currently available for Stata and SPSS, yet there is little guidance for authors regarding the practical application and implementation of those macros. This paper provides a brief tutorial on the implementation of the Stata and SPSS macros and discusses practical issues meta-analysts should consider when estimating meta-regression models with robust variance estimates. Two example databases are used in the tutorial to illustrate the use of meta-analysis with robust variance estimates. 

Handling complex meta-analytic data structures using robust variance estimates: A tutorial in R

Identifying and understanding causal risk factors for crime over the life-course is a key area of inquiry in developmental criminology. Prospective longitudinal studies provide valuable information about the relationships between risk factors and later criminal offending. Meta-analyses that synthesize findings from these studies can summarize the predictive strength of different risk factors for crime, and offer unique opportunities for examining the developmental variability of risk factors. Complex data structures are common in such meta-analyses, whereby primary studies provide multiple (dependent) effect sizes.

Standardized mean difference effect sizes for single-case designs: A primer and tutorial using R

In this paper we discuss the development and use of between-case standardized mean difference effect sizes for two popular single-case research designs (the treatment reversal design and the multiple baseline design), and discuss how they might be used in meta-analyses either with other single-case research designs or in conjunction with between-group research designs.

Using machine learning to identify and investigate moderators of alcohol use intervention effects in meta-analyses

The aim of this paper is to illustrate a machine learning-based approach for identifying and investigating moderators of alcohol use intervention effects in aggregate-data meta-analysis. We demonstrate the machine learning technique of random forest modeling using data from an ongoing meta-analysis of brief substance use interventions implemented in general healthcare settings.

Network meta-analysis techniques for synthesizing prevention science evidence

This paper presents a primer of network meta-analysis for prevention scientists who wish to apply this method or to critically appraise evidence from publications using the method. We introduce the key concepts and assumptions of network meta-analysis, namely, transitivity and consistency, and demonstrate their applicability to the field of prevention science. We then illustrate the method using a network meta-analysis examining the comparative effectiveness of brief alcohol interventions for preventing hazardous drinking among college students. We provide data and code for all examples. Finally, we discuss considerations that are particularly relevant in network meta-analyses in the field of prevention, such as including non-randomized evidence.

Recently Edited Special Issues

Meta-Analyses and Systematic Reviews for Alcohol Research

This special issue of Alcohol and Alcoholism includes a series of articles on novel methodological and analytical techniques that can be applied to future evidence syntheses as well as systematic reviews and meta-analyses that stand to make important contributions to the field of alcohol research.

Modern Meta-Analytic Methods in Prevention Science

This special issue of Prevention Science presents applied demonstrations of modern meta-analytic techniques in order to increase accessability and facilitate the use of these techniques, with an ultimate goal of accelerating the uptake of modern meta-analytic methods in the field of prevention science.

Brief Alcohol Interventions for Young Adults

This special issue of Psychology of Addictive Behaviors addresses innovative methods to personalize and strengthen the magnitude and durability of brief intervention effects, considerations of intervention components and technology enhancements, and implementation approaches for producing population-level changes in alcohol use among young adults.

Parenting in the Context of Opioid Use

This special issue of Frontiers in Psychology focuses on increasing the understanding of associations between parenting and opioid use by exploring the complex systems in which neurobiological, psychological, social, and structural features interact, detailing the development and testing of new prevention and intervention programs, and paying attention to diverse parent types and family structures. 

Evidence Reviews

HEDCO Institute Evidence Hub

The HEDCO Institute is dedicated to helping educators and school administrators tackle complex decisions on the best ways to support their students’ mental and behavioral health. Their products are designed to translate educational research into accessible, usable products that meet the ever-changing decision-making needs of K-12 education leaders.

What Works Clearinghouse

For more than a decade, the WWC has been a central and trusted source of scientific evidence on education programs, products, practices, and policies. They review the research, determine which studies meet rigorous standards, and summarize the findings to answer the question “what works in education?”

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