Project INTEGRATE: An integrative study of brief alcohol interventions for college students.

Abstract

This paper provides an overview of a study that synthesizes multiple, independently collected alcohol intervention studies for college students into a single, multisite longitudinal data set. This research embraced innovative analytic strategies (i.e., integrative data analysis or meta-analysis using individual participant-level data), with the overall goal of answering research questions that are difficult to address in individual studies such as moderation analysis, while providing a built-in replication for the reported efficacy of brief motivational interventions for college students. Data were pooled across 24 intervention studies, of which 21 included a comparison or control condition and all included one or more treatment conditions. This yielded a sample of 12,630 participants (42% men; 58% first-year or incoming students). The majority of the sample identified as White (74%), with 12% Asian, 7% Hispanic, 2% Black, and 5% other/mixed ethnic groups. Participants were assessed two or more times from baseline up to 12 months, with varying assessment schedules across studies. This paper describes how we combined individual participant-level data from multiple studies, and discusses the steps taken to develop commensurate measures across studies via harmonization and newly developed Markov chain Monte Carlo algorithms for two-parameter logistic item response theory models and a generalized partial credit model. This innovative approach has intriguing promises, but significant barriers exist. To lower the barriers, there is a need to increase overlap in measures and timing of follow-up assessments across studies, better define treatment and control groups, and improve transparency and documentation in future single, intervention studies.

Reviewing and interpreting the effects of brief alcohol interventions: comment on a Cochrane review about motivational interviewing for young adults

“…These conclusions were surprising, as they are discrepant with conclusions of other reviews on similar interventions with similar effect sizes 1, 14, 15. This review's conclusions indicate that the field should discuss what should be expected from brief interventions and how best to use them—or to caution their dissemination and focus more research on alternative intervention approaches, as suggested by a recent individual participant data (IPD) meta‐analysis of a subset of trials evaluating brief motivational interventions for college student drinking 16, 17. Careful interpretation of intervention effects is crucial to using systematic reviews for informing future research, policy and practice, as summary conclusions are more likely to attract attention than specific effect sizes from meta‐analyses 18, 19, 20, 21.…”

Associations Between Alcohol Use and Alcohol-Related Negative Consequences Among Black and White College Men and Women

“…Data came from Project INTEGRATE (Mun et al, 2011). Of the 24 studies included in the Project INTEGRATE data set, pooled data from 15 independent studies conducted at public and private universities across the United States were collectively analyzed in the present study because these studies included the RAPI items.…”

A Hierarchical Multi-Unidimensional IRT Approach for Analyzing Sparse, Multi-Group Data for Integrative Data Analysis

“…As we briefly discussed earlier, we developed the 2PL-MUIRT model to establish a commensurate metric across different studies pooled for Project INTEGRATE (Mun et al, 2011). Project INTEGRATE was launched to overcome existing methodological limitations of individual studies, such as lack of sufficient sample size and homogeneous samples, in the field of college alcohol intervention research.…”

“…The current study was motivated by a large-scale IDA study, Project INTEGRATE (Mun et al, 2011), which combined data ( N = 24,336) from 24 independent alcohol intervention studies. We proposed a hierarchical, two-parameter multi-unidimensional logistic item response theory (2PL-MUIRT) model extended for multiple groups (or studies) and developed new Markov chain Monte Carlo (MCMC) algorithms from a hierarchical Bayesian perspective, which is an extension from the existing work by de la Torre and Patz (2005) on the 3PL-MUIRT model.…”