RTI uses cookies to offer you the best experience online. By clicking “accept” on this website, you opt in and you agree to the use of cookies. If you would like to know more about how RTI uses cookies and how to manage them please view our Privacy Policy here. You can “opt out” or change your mind by visiting: http://optout.aboutads.info/. Click “accept” to agree.
Introduction to the special issue on innovations and applications of integrative data analysis (IDA) and related data harmonization procedures in prevention science
Morgan-López, A. A., Bradshaw, C. P., & Musci, R. J. (2023). Introduction to the special issue on innovations and applications of integrative data analysis (IDA) and related data harmonization procedures in prevention science. Prevention Science, 24(8), 1425-1434. https://doi.org/10.1007/s11121-023-01600-7
This paper serves as an introduction to the special issue of Prevention Science entitled, "Innovations and Applications of Integrative Data Analysis (IDA) and Related Data Harmonization Procedures in Prevention Science." This special issue includes a collection of original papers from multiple disciplines that apply individual-level data synthesis methodologies, including IDA, individual participant meta-analysis, and other related methods to harmonize and integrate multiple datasets from intervention trials of the same or similar interventions. This work builds on a series of papers appearing in a prior Prevention Science special issue, entitled "Who Benefits from Programs to Prevent Adolescent Depression?" (Howe, Pantin, & Perrino, 2018). Since the publication of this prior work, the use of individual-level data synthesis has increased considerably in and outside of prevention. As such, there is a need for an update on current and future directions in IDA, with careful consideration of innovations and applications of these methods to fill important research gaps in prevention science. The papers in this issue are organized into two broad categories of (1) evidence synthesis papers that apply best practices in data harmonization and individual-level data synthesis and (2) new and emerging design, psychometric, and methodological issues and solutions. This collection of original papers is followed by two invited commentaries which provide insight and important reflections on the field and future directions for prevention science.