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

A Randomized Controlled Trial of Counterfactual and Personalized Normative Feedback Interventions to Increase Safe Drinking Strategies

Objective: A variety of interventions have been developed to target problematic college student drinking, with minimal long-term effects. Several interventions incorporate alcohol protective behavioral strategies (PBS); however, these remain underdeveloped and underused. Counterfactuals (thoughts about what might have been) can strengthen causal links between behavior and outcome. Recent research has demonstrated counterfactual's effectiveness to increase PBS use. This study examines the effects of brief counterfactual and personalized normative feedback (PNF) interventions to increase PBS use. Method: Undergraduates (N = 239) completed an online 6-week intervention. Participants completed baseline demographics, PBS and alcohol use, and alcohol consequences measures. Participants were randomly assigned to condition: negative event only (Control), negative event with details (Sham), negative event with counterfactuals (CF), and PNF (PBS-PNF). They reported alcohol use, alcohol consequences, and PBS use weekly for 6 wk. Results: PBS use declined in both Control and Sham conditions. By the study's conclusion, CF (d = 0.54) and PBS-PNF (d = 0.46) conditions reported significantly higher PBS use (vs. Control). The Control, Sham, and PBS-PNF (but not CF) conditions had increases in alcohol use across time. In the final week there were indirect associations between alcohol consequences and both CF and PBS-PNF conditions from five weeks prior. Conclusions: This study provides some support for the use of both personalized normative feedback and counterfactual interventions to increase PBS use in college students. The personal relevance of the counterfactual intervention may produce sustained positive outcomes.

A simulation study of the performance of statistical models for count outcomes with excessive zeros

BACKGROUND

Outcome measures that are count variables with excessive zeros are common in health behaviors research. Examples include the number of standard drinks consumed or alcohol-related problems experienced over time. There is a lack of empirical data about the relative performance of prevailing statistical models for assessing the efficacy of interventions when outcomes are zero-inflated, particularly compared with recently developed marginalized count regression approaches for such data.

METHODS

The current simulation study examined five commonly used approaches for analyzing count outcomes, including two linear models (with outcomes on raw and log-transformed scales, respectively) and three prevailing count distribution-based models (ie, Poisson, negative binomial, and zero-inflated Poisson (ZIP) models). We also considered the marginalized zero-inflated Poisson (MZIP) model, a novel alternative that estimates the overall effects on the population mean while adjusting for zero-inflation. Motivated by alcohol misuse prevention trials, extensive simulations were conducted to evaluate and compare the statistical power and Type I error rate of the statistical models and approaches across data conditions that varied in sample size (N = 100 $$ N=100 $$ to 500), zero rate (0.2 to 0.8), and intervention effect sizes.

RESULTS

Under zero-inflation, the Poisson model failed to control the Type I error rate, resulting in higher than expected false positive results. When the intervention effects on the zero (vs. non-zero) and count parts were in the same direction, the MZIP model had the highest statistical power, followed by the linear model with outcomes on the raw scale, negative binomial model, and ZIP model. The performance of the linear model with a log-transformed outcome variable was unsatisfactory.

CONCLUSIONS

The MZIP model demonstrated better statistical properties in detecting true intervention effects and controlling false positive results for zero-inflated count outcomes. This MZIP model may serve as an appealing analytical approach to evaluating overall intervention effects in studies with count outcomes marked by excessive zeros.

Designing and testing treatments for alcohol use disorder

This chapter provides a succinct overview of several recommendations for the design and analysis of treatments for AUD with a specific focus on increasing rigor and generalizability of treatment studies in order to increase the reach of AUD treatment. We recommend that researchers always register their trials in a clinical trial registry and make the protocol accessible so that the trial can be replicated in future work, follow CONSORT reporting guidelines when reporting the results of the trial, carefully describe all inclusion and exclusion criteria as well as the randomization scheme, and always use an intent to treat design with attention to analysis of missing data. In addition, we recommend that researchers pay closer attention to recruitment and engagement strategies that increase enrollment and retention of historically marginalized and understudied populations, and we end with a plea for more consideration of implementation science approaches to increase the dissemination and implementation of AUD treatment in real-world settings.

The selection of statistical models for reporting count outcomes and intervention effects in brief alcohol intervention trials: A review and recommendations

Understanding the efficacy and relative effectiveness of a brief alcohol intervention (BAI) relies on obtaining a credible intervention effect estimate. Outcomes in BAI trials are often count variables, such as the number of drinks consumed, which may be overdispersed (i.e., greater variability than expected based on a given model) and zero-inflated (i.e., greater probability of zeros than expected based on a given model). Ignoring such distribution characteristics can lead to biased estimates and invalid statistical conclusions. In this critical review, we identified and reviewed 64 articles that reported count outcomes from a systematic review of BAI trials for adolescents and young adults from 2013 to 2018. Given many statistical models to choose from when analyzing count outcomes, we reviewed the models used and reporting practices in the BAI trial literature. A majority (61.3%) of analyses with count outcomes used linear models despite violations of normality assumptions; 75.6% of outcome variables demonstrated clear overdispersion. We provide an overview of available count models (Poisson, negative binomial, zero-inflated or hurdle, and marginalized zero-inflated Poisson regression) and formulate practical guidelines for reporting outcomes of BAIs. We provide a visual step-by-step decision guide for selecting appropriate statistical models and reporting results for count outcomes. We list accessible resources to help researchers select an appropriate model with which to analyze their data. Recent advances in count distribution-based models hold promise for evaluating count outcomes to gauge the efficacy and effectiveness of BAIs and identify critical covariates in alcohol epidemiologic research. We recommend that researchers report the distributional properties of count outcomes, such as the proportion of zero counts, and select an appropriate statistical analysis for count outcomes using the provided decision tree. By following these recommendations, future research may yield more accurate, transparent, and reproducible results.

Brief Alcohol Interventions are Effective through 6 Months: Findings from Marginalized Zero-inflated Poisson and Negative Binomial Models in a Two-step IPD Meta-analysis

To evaluate and optimize brief alcohol interventions (BAIs), it is critical to have a credible overall effect size estimate as a benchmark. Estimating such an effect size has been challenging because alcohol outcomes often represent responses from a mixture of individuals: those at high risk for alcohol misuse, occasional nondrinkers, and abstainers. Moreover, some BAIs exclusively focus on heavy drinkers, whereas others take a universal prevention approach. Depending on sample characteristics, the outcome distribution might have many zeros or very few zeros and overdispersion; consequently, the most appropriate statistical model may differ across studies. We synthesized individual participant data (IPD) from 19 studies in Project INTEGRATE (Mun et al., 2015b) that randomly allocated participants to intervention and control groups (N = 7,704 participants, 38.4% men, 74.7% White, 58.5% first-year students). We sequentially estimated marginalized zero-inflated Poisson (Long et al., 2014) or negative binomial regression models to obtain covariate-adjusted, study-specific intervention effect estimates in the first step, which were subsequently combined in a random-effects meta-analysis model in the second step. BAIs produced a statistically significant 8% advantage in the mean number of drinks at both 1-3 months (RR = 0.92, 95% CI = [0.85, 0.98]) and 6 months (RR = 0.92, 95% CI = [0.85, 0.99]) compared to controls. At 9-12 months, there was no statistically significant difference in the mean number of drinks between BAIs and controls. In conclusion, BAIs are effective at reducing the mean number of drinks through at least 6 months post intervention. IPD can play a critical role in deriving findings that could not be obtained in original individual studies or standard aggregate data meta-analyses.

Harmonizing Social, Emotional, and Behavioral Constructs in Prevention Science: Digging into the Weeds of Aligning Disparate Measures

While integrative data analysis (IDA) presents great opportunity, it also necessitates a myriad of methodological decisions related to harmonizing disparate measures collected across multiple studies. There is a lack of step-by-step methodological guidance for harmonizing disparate measures of latent constructs differently conceptualized or operationalized across studies, such as social, emotional, and behavioral constructs often utilized in prevention science. The current paper addressed this gap by providing methodological guidance and a case illustration focused on harmonizing measures of disparately conceptualized and operationalized constructs. We do so by outlining a five-phased harmonization approach paired with an illustrative example of the approach as applied to harmonization of broadband latent emotional and behavioral health constructs assessed with different measures across studies. This approach builds on and expands upon procedures currently recommended in the IDA literature with parallels to best practices in test development procedures. The illustrative example of our phased approach is drawn from an IDA study of 11 randomized controlled trials of Coping Power (Lochman & Wells, 2004), an evidence-based preventive intervention. We demonstrate the harmonization of two constructs, internalizing and externalizing problems, as harmonized across the teacher-reported scales of the Achenbach System of Empirically Based Assessment (Achenbach, 1991a) and the Behavior Assessment System for Children (Reynolds & Kamphaus, 2004). Finally, we consider the potential strengths and limitations of this phased approach, underscoring areas for future methodological research and conclude with some recommendations.

Which is Better for Individual Participant Data Meta-Analysis of Zero-Inflated Count Outcomes, One-Step or Two-Step Analysis? A Simulation Study

Meta-analysis using individual participant data (IPD) is an important methodology in intervention research because it (a) increases accuracy and precision of estimates, (b) allows researchers to investigate mediators and moderators of treatment effects, and (c) makes use of extant data. IPD meta-analysis can be conducted either via a one-step approach that uses data from all studies simultaneously, or a two-step approach, which aggregates data for each study and then combines them in a traditional meta-analysis model. Unfortunately, there are no evidence-based guidelines for how best to approach IPD meta-analysis for count outcomes with many zeroes, such as alcohol use. We used simulation to compare the performance of four hurdle models (3 one-step and 1 two-step models) for zero-inflated count IPD, under realistic data conditions. Overall, all models yielded adequate coverage and bias for the treatment effect in the count portion of the model, across all data conditions. However, in the zero portion, the treatment effect was underestimated in most models and data conditions, especially when there were fewer studies. The performance of both one- and two-step approaches depended on the formulation of the treatment effects, suggesting a need to carefully consider model assumptions and specifications when using IPD.

Integrative data analysis of clinical trials network studies to examine the impact of psychosocial treatments for black people who use cocaine: Study protocol

BACKGROUND

Cocaine overdose death rates among Black people are higher than that of any other racial/ethnic group, attributable to synthetic opioids in the cocaine supply. Understanding the most effective psychostimulant use treatment interventions for Black people is a high priority. While some interventions have proven effective for the general population, their comparative effectiveness among Black people remains unknown. To address this gap, our NIDA-funded Clinical Trials Network (CTN) study (0125), will use Integrative Data Analysis (IDA) to examine treatment effectiveness across 9 CTN studies. This manuscript describes the study protocol for CTN-0125.

METHODS

Of the 59 completed randomized clinical trials in the CTN with available datasets, nine met our inclusion criteria: 1) behavioral intervention, 2) targeted cocaine use or use disorder, 3) included sub-samples of participants who self-identified as Black and 4) included outcome measures of cocaine and psychostimulant use and consequences. We aim to 1) estimate scale scores of cocaine use severity while considering study-level measurement non-invariance, 2) compare the effectiveness of psychosocial treatments for psychostimulant use, and 3) explore individual (e.g., concomitant opioid use, age, sex, employment, pre-treatment psychiatric status) and study-level moderators (e.g., attendance/retention) to evaluate subgroup differences in treatment effectiveness.

CONCLUSION

The NIDA CTN provides a unique collection of studies that can offer insight into what interventions are most efficacious for Black people. Findings from our CTN-0125 study have the potential to substantially inform treatment approaches specifically designed for Black people who use psychostimulants.

Do brief motivational interventions increase motivation for change in drinking among college students? A two-step meta-analysis of individual participant data

BACKGROUND

Brief motivational interventions (BMIs) are one of the most effective individually focused alcohol intervention strategies for college students. Despite the central theoretical role of motivation for change in BMIs, it is unclear whether BMIs increase motivation to change drinking behavior. We conducted a two-step meta-analysis of individual participant data (IPD) to examine whether BMIs increase motivation for change. N = 5903;59% women, 72% White) from Project INTEGRATE. The BMIs included individually delivered motivational interviewing with personalized feedback (MI + PF), stand-alone personalized feedback (PF), and group-based motivational interviewing (GMI).

METHODS

We included 15 trials of BMI (N = 5903;59% women, 72% White) from Project INTEGRATE. The BMIs included individually-delivered motivational interviewing with personalized feedback (MI + PF), stand-alone personalized feedback (PF), and group-based motivational interviewing (GMI). Different measures and responses used in the original trials were harmonized. Effect size estimates were derived from a model that adjusted for baseline motivation and demographic variables for each trial (step 1) and subsequently combined in a random-effects meta-analysis (step 2).

RESULTS

The overall intervention effect of BMIs on motivation for change was not statistically significant (standard mean difference [SMD]: 0.026, 95% CI: [-0.001, 0.053], p = 0.06, k = 19 comparisons). Of the three subtypes of BMIs, GMI, which tended to provide motivation-targeted content, had a statistically significant intervention effect on motivation, compared with controls (SMD: 0.055, 95% CI: [0.007, 0.103], p = 0.025, k = 5). By contrast, there was no evidence that MI + PF (SMD = 0.04, 95% CI: [-0.02, 0.10], k = 6, p = 0.20) nor PF increased motivation (SMD = 0.005, 95% CI: [-0.028, 0.039], k = 8, p = 0.75), compared with controls. Post hoc meta-regression analysis suggested that motivation sharply decreased each month within the first 3 months postintervention (b = -0.050, z = -2.80, p = 0.005 for k = 14).

CONCLUSIONS

Although BMIs provide motivational content and normative feedback and are assumed to motivate behavior change, the results do not wholly support the hypothesis that BMIs improve motivation for change. Changing motivation is difficult to assess during and following interventions, but it is still a theoretically important clinical endpoint. Further, the evidence cautiously suggests that changing motivation may be achievable, especially if motivation-targeted content components are provided.

A randomized pilot trial of a mobile delivered brief motivational interviewing and behavioral economic alcohol intervention for emerging adults

OBJECTIVE

Mobile health (mHealth) interventions show potential to broaden the reach of efficacious alcohol brief motivational interventions (BMIs). However, efficacy is mixed and may be limited by low participant attention and engagement. The present study examined the feasibility, acceptability, and preliminary efficacy of a live text-message delivered BMI in a pilot randomized clinical trial.

METHOD

Participants were 66 college students (63.6% women; 61.9% White; Mage = 19.95, SD = 1.66) reporting an average of 11.88 (SD = 8.74) drinks per week, 4.42 (SD = 3.59) heavy drinking episodes (HDEs), and 8.44 (SD = 5.62) alcohol-related problems in the past month. Participants were randomized to receive either (a) education or (b) an alcohol BMI plus behavioral economic substance-free activity session (SFAS), each followed by 4 weeks of mini sessions. All sessions were administered via live text-message. Participants completed assessments postintervention (after the 4th mini session) and at 3-month follow-up.

RESULTS

90.9% completed both initial full-length sessions and at least two of the four mini sessions with 87.9% retention at 3-month follow-up. Participants found the interventions useful, interesting, relevant, and effective, with no between-group differences. There were no statistically significant group differences in drinks per week or alcohol-related problems at follow-up, but BMI + SFAS participants reported fewer past-month HDEs than those who received education.

CONCLUSIONS

Live text-messaging to deliver the BMI + SFAS is feasible and well-received. The preliminary efficacy results should be interpreted cautiously due to the small sample size but support further investigation. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Data Quality and Study Compliance Among College Students Across 2 Recruitment Sources: Two Study Investigation

BACKGROUND

Models of satisficing suggest that study participants may not fully process survey items and provide accurate responses when survey burden is higher and when participant motivation is lower. Participants who do not fully process survey instructions can reduce a study's power and hinder generalizability. Common concerns among researchers using self-report measures are data quality and participant compliance. Similarly, attrition can hurt the power and generalizability of a study.

OBJECTIVE

Given that college students comprise most samples in psychological studies, especially examinations of student issues and psychological health, it is critical to understand how college student recruitment sources impact data quality (operationalized as attention check items with directive instructions and correct answers) and retention (operationalized as the completion of follow-up surveys over time). This examination aimed to examine the following: whether data quality varies across recruitment sources, whether study retention varies across recruitment sources, the impact of data quality on study variable associations, the impact of data quality on measures of internal consistency, and whether the demographic qualities of participants significantly vary across those who failed attention checks versus those who did not.

METHODS

This examination was a follow-up analysis of 2 previously published studies to explore data quality and study compliance. Study 1 was a cross-sectional, web-based survey examining college stressors and psychological health (282/407, 69.3% female; 230/407, 56.5% White, 113/407, 27.8% Black; mean age 22.65, SD 6.73 years). Study 2 was a longitudinal college drinking intervention trial with an in-person baseline session and 2 web-based follow-up surveys (378/528, 71.6% female; 213/528, 40.3% White, 277/528, 52.5% Black; mean age 19.85, SD 1.65 years). Attention checks were included in both studies to assess data quality. Participants for both studies were recruited from a psychology participation pool (a pull-in method; for course credit) and the general student body (a push-out method; for monetary payment or raffle entry).

RESULTS

A greater proportion of participants recruited through the psychology pool failed attention checks in both studies, suggesting poorer data quality. The psychology pool was also associated with lower retention rates over time. After screening out those who failed attention checks, some correlations among the study variables were stronger, some were weaker, and some were fairly similar, potentially suggesting bias introduced by including these participants. Differences among the indicators of internal consistency for the study measures were negligible. Finally, attention check failure was not significantly associated with most demographic characteristics but varied across some racial identities. This suggests that filtering out data from participants who failed attention checks may not limit sample diversity.

CONCLUSIONS

Investigators conducting college student research should carefully consider recruitment and include attention checks or other means of detecting poor quality data. Recommendations for researchers are discussed.

Protective behavioral strategies are more helpful for avoiding alcohol-related problems for college drinkers who drink less

Objective: To examine race, gender, and alcohol use level as moderators of the association between protective behavioral strategies (PBS) and alcohol-related problems. Participants: A sample of 12,011 participants who reported recent drinking (87.7% White, 61% Women) from Project INTEGRATE, a study that combined individual participant data (IPD) from 24 brief motivational intervention trials for college students. Methods: Hierarchical regressions were conducted to determine whether there was a moderated effect of PBS on alcohol problems across alcohol use levels, and whether the moderated protective effect of PBS by alcohol use differed by gender and race. Results: The protective association between PBS and alcohol-related problems was greater for those who drank less. This moderated effect did not differ across men and women or across racial groups. Conclusions: College drinking prevention programs should ensure that students are aware of the limits of PBS as a mitigator of alcohol problems.

Design and methodology for an integrative data analysis of coping power: Direct and indirect effects on adolescent suicidality

As suicide rates have risen in the last decade, there has been greater emphasis on targeting early risk conditions for suicidality among youth and adolescents as a form of suicide "inoculation". Two particular needs that have been raised in this nascent literature are a) the dearth of examination of early intervention effects on distal suicide risk that target externalizing behaviors and b) the need to harmonize multiple existing intervention datasets for greater precision in modeling intervention effects on low base rate outcomes such as suicidal behaviors. This project, entitled "Integrative Data Analysis of Coping Power (CP): Effects on Adolescent Suicidality", funded by the National Institute of Mental Health (NIMH), will harmonize and analyze data from 11 randomized controlled trials of CP (total individual-level N = 3183, total school-level N = 189). CP is an empirically-supported, child- and family-focused preventive intervention that focuses on reducing externalizing more broadly among youth who exhibit early aggression, which makes it ideally suited to targeting externalizing pathways to suicidality. The project utilizes three measurement and data analysis frameworks that have emerged across multiple independent disciplines: integrative data analysis (IDA), random treatment effects multilevel modeling (RTE-MLM), and propensity score weighting (PSW). If successful, the project will a) provide initial evidence that CP would have gender-specific indirect effects on suicidality through reductions in externalizing for boys and reductions in internalizing for girls and b) identify optimal conditions under which CP is delivered (e.g., groups, individuals, online) across participants on reductions in suicidality and other key intermediate endpoints.

Modern Meta-Analytic Methods in Prevention Science: Introduction to the Special Issue

Meta-analyses that statistically synthesize evidence from multiple research studies can play an important role in advancing evidence-informed prevention science. When done in the context of a well-conducted systematic review, meta-analysis is a powerful tool for synthesizing evidence and exploring research questions that are difficult to address in individual studies, such as the association of individual study limitations on intervention effect estimates, replicability of empirical findings, and variation of effect estimates across populations and settings. Alongside the rapid growth in the number of published reviews and meta-analyses, there has been a parallel growth in the development of meta-analytic techniques to handle the increasingly complex types of questions and types of evidence relevant to prevention science. Despite this rapid evolution of meta-analytic techniques and approaches, there is still a lag between the development of new techniques and their uptake by researchers in the field. This paper serves as a brief introduction to this special issue of Prevention Science, entitled "Modern Meta-Analytic Methods in Prevention Science," which highlights recent developments in meta-analytic methods and demonstrates their application to prevention research. This special issue makes an important contribution to the field by ensuring these methodological advances are widely accessible to prevention science researchers, thereby improving their uptake and utilization, and ultimately improving the utility and rigor of research syntheses for informing evidence-based decision making in prevention.

A Tutorial on Cognitive Diagnosis Modeling for Characterizing Mental Health Symptom Profiles Using Existing Item Responses

In research applications, mental health problems such as alcohol-related problems and depression are commonly assessed and evaluated using scale scores or latent trait scores derived from factor analysis or item response theory models. This tutorial paper demonstrates the use of cognitive diagnosis models (CDMs) as an alternative approach to characterizing mental health problems of young adults when item-level data are available. Existing measurement approaches focus on estimating the general severity of a given mental health problem at the scale level as a unidimensional construct without accounting for other symptoms of related mental health problems. The prevailing approaches may ignore clinically meaningful presentations of related symptoms at the item level. The current study illustrates CDMs using item-level data from college students (40 items from 719 respondents; 34.6% men, 83.9% White, and 16.3% first-year students). Specifically, we evaluated the constellation of four postulated domains (i.e., alcohol-related problems, anxiety, hostility, and depression) as a set of attribute profiles using CDMs. After accounting for the impact of each attribute (i.e., postulated domain) on the estimates of attribute profiles, the results demonstrated that when items or attributes have limited information, CDMs can utilize item-level information in the associated attributes to generate potentially meaningful estimates and profiles, compared to analyzing each attribute independently. We introduce a novel visual inspection aid, the lens plot, for quantifying this gain. CDMs may be a useful analytical tool to capture respondents' risk and resilience for prevention research.

Behavioral economics of substance use: Understanding and reducing harmful use during the COVID-19 pandemic

Behavioral economic research demonstrates that alcohol and drug consumption is (a) an inverse function of constraints on access to the substance and (b) a direct function of constraints on access to alternative rewards. Physical distancing interventions and economic consequences of the COVID-19 pandemic have resulted in unprecedented reductions in many of the constraints on substance use and in critical evolutionarily salient sources of alternative reward, such as social interaction, physical activity, leisure activities and hobbies, and academic and occupational pursuits. Thus, behavioral economics suggests that the pandemic and necessary public health response have created a "perfect storm" for exacerbation of individual-level and population-level substance use problems and also points to multilevel intervention strategies. We summarize this perspective and research by highlighting 3 critical behavioral processes that will influence drug and alcohol consumption. First, the sudden absence of many effective constraints on substance use (work, school, community, or service obligations) will reduce the actual and perceived cost of use. Second, physical distancing measures will reduce the availability, and increase the cost, of many rewarding substance-free activities and commodities. Third, increased uncertainty around current and future events increases discounting of delayed rewards. These effects will be especially pernicious among populations with existing health disparities. Next, we outline interventions suggested by behavioral economics to mitigate the impact of COVID-19 on substance use that are aimed at increasing perceived costs of use; increasing access to substance-free activities, including treatment; and lengthening the timeframe for behavioral allocation and altering environmental contexts to promote healthy choices. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A bias correction method in meta-analysis of randomized clinical trials with no adjustments for zero-inflated outcomes

Many clinical endpoint measures, such as the number of standard drinks consumed per week or the number of days that patients stayed in the hospital, are count data with excessive zeros. However, the zero‐inflated nature of such outcomes is sometimes ignored in analyses of clinical trials. This leads to biased estimates of study‐level intervention effect and, consequently, a biased estimate of the overall intervention effect in a meta‐analysis. The current study proposes a novel statistical approach, the Zero‐inflation Bias Correction (ZIBC) method, that can account for the bias introduced when using the Poisson regression model, despite a high rate of inflated zeros in the outcome distribution of a randomized clinical trial. This correction method only requires summary information from individual studies to correct intervention effect estimates as if they were appropriately estimated using the zero‐inflated Poisson regression model, thus it is attractive for meta‐analysis when individual participant‐level data are not available in some studies. Simulation studies and real data analyses showed that the ZIBC method performed well in correcting zero‐inflation bias in most situations.

A Structural Equation Modeling Approach to Meta-analytic Mediation Analysis Using Individual Participant Data: Testing Protective Behavioral Strategies as a Mediator of Brief Motivational Intervention Effects on Alcohol-Related Problems

This paper introduces a meta-analytic mediation analysis approach for individual participant data (IPD) from multiple studies. Mediation analysis evaluates whether the effectiveness of an intervention on health outcomes occurs because of change in a key behavior targeted by the intervention. However, individual trials are often statistically underpowered to test mediation hypotheses. Existing approaches for evaluating mediation in the meta-analytic context are limited by their reliance on aggregate data; thus, findings may be confounded with study-level differences unrelated to the pathway of interest. To overcome the limitations of existing meta-analytic mediation approaches, we used a one-stage estimation approach using structural equation modeling (SEM) to combine IPD from multiple studies for mediation analysis. This approach (1) accounts for the clustering of participants within studies, (2) accommodates missing data via multiple imputation, and (3) allows valid inferences about the indirect (i.e., mediated) effects via bootstrapped confidence intervals. We used data (N = 3691 from 10 studies) from Project INTEGRATE (Mun et al. Psychology of Addictive Behaviors, 29, 34–48, 2015) to illustrate the SEM approach to meta-analytic mediation analysis by testing whether improvements in the use of protective behavioral strategies mediate the effectiveness of brief motivational interventions for alcohol-related problems among college students. To facilitate the application of the methodology, we provide annotated computer code in R and data for replication. At a substantive level, stand-alone personalized feedback interventions reduced alcohol-related problems via greater use of protective behavioral strategies; however, the net-mediated effect across strategies was small in size, on average.

Integrative data analysis of self-efficacy in 4 clinical trials for alcohol use disorder

BACKGROUND

Self-efficacy has been proposed as a key predictor of alcohol treatment outcomes and a potential mechanism of success in achieving abstinence or drinking reductions following alcohol treatment. Integrative data analysis, where data from multiple studies are combined for analyses, can be used to synthesize analyses across multiple alcohol treatment trials by creating a commensurate measure and controlling for differential item functioning (DIF) to determine whether alcohol treatments improve self-efficacy.

METHOD

The current study used moderated nonlinear factor analysis (MNLFA) to examine the effect of treatment on self-efficacy across four different treatment studies (N = 3720; 72.5% male, 68.4% non-Hispanic white). Self-efficacy was measured using the Alcohol Abstinence Self-Efficacy Scale (AASE) in the COMBINE Study (n = 1383) and Project MATCH (n = 1726), and the Drug Taking Confidence Questionnaire (DTCQ) in two studies of Telephone Continuing Care (TEL Study 1: n = 303; TEL Study 2: n = 212). DIF was examined across time, study, treatment condition, marital status, age, and sex.

RESULTS

We identified 12 items from the AASE and DTCQ to create a commensurate measure of self-efficacy using MNLFA. All active treatments, including cognitive-behavioral treatment, a combined behavioral intervention, medication management, motivation enhancement treatment, telephone continuing care, twelve-step facilitation, and relapse prevention, were associated with significant increases in self-efficacy from baseline to posttreatment that were maintained for up to a year. Importantly, treatment as usual in community settings, which consisted of weekly group therapy that included addiction counseling and twelve-step recovery support, was not associated with significant increases in self-efficacy.

CONCLUSIONS

Alcohol self-efficacy increases following treatment and numerous evidence-based treatments are associated with significant increases in self-efficacy, which are maintained over time. Community treatment that focuses solely on addiction counseling and twelve-step support may not promote increases in self-efficacy.

Synthesizing Indirect Effects in Mediation Models With Meta-Analytic Methods

AIMS

A mediator is a variable that explains the underlying mechanism between an independent variable and a dependent variable. The indirect effect indicates the effect from the predictor to the outcome variable via the mediator. In contrast, the direct effect represents the predictor's effort on the outcome variable after controlling for the mediator.

METHODS

A single study rarely provides enough evidence to answer research questions in a particular domain. Replications are generally recommended as the gold standard to conduct scientific research. When a sufficient number of studies have been conducted addressing similar research questions, a meta-analysis can be used to synthesize those studies' findings.

RESULTS

The main objective of this paper is to introduce two frameworks to integrating studies using mediation analysis. The first framework involves calculating standardized indirect effects and direct effects and conducting a multivariate meta-analysis on those effect sizes. The second one uses meta-analytic structural equation modeling to synthesize correlation matrices and fit mediation models on the average correlation matrix. We illustrate these procedures on a real dataset using the R statistical platform.

CONCLUSION

This paper closes with some further directions for future studies.

Evaluating treatments for posttraumatic stress disorder, alcohol and other drug use disorders using meta-analysis of individual patient data: Design and methodology of a virtual clinical trial

This paper describes Project Harmony, a Virtual Clinical Trial (VCT) funded by the National Institute on Alcohol Abuse and Alcoholism (NIAAA) to harmonize and analyze data from over 40 independent psychological, pharmacologic and/or combined pharmacological treatment studies for posttraumatic stress disorder and comorbid alcohol and other drug use disorders (PTSD/AOD). The study attends to three distinct analysis challenges: (1) variation in measurement of PTSD/AOD across studies, time, populations and reporters, (2) cross-study variation in treatment effect sizes and (3) non-randomized, cross-study variation in the classification of treatments (despite within-study randomization of treatment arms). To address these challenges, the study combines meta-analysis of individual patient data (MIPD), integrative data analysis (IDA) and propensity score weighting (PSW) to integrate raw data from these clinical trials. This protocol shows how this VCT analytic framework was used to (1) develop commensurate scale scores of PTSD and AOD severity when measures vary across studies, (2) compare the efficacy of evidence-based treatment models for PTSD/AOD, (3) test for potential mediators of treatment effects on AOD and PTSD across treatment models, and (4) explore individual- and study-level moderators to inform for whom each of the treatment models works best. The advantages of the general VCT approach are juxtaposed against the limitations of single randomized controlled trials and conventional meta-analysis.

Do Brief Alcohol Interventions Reduce Driving After Drinking Among College Students? A Two-step Meta-analysis of Individual Participant Data

Aims

College students who drink are at an increased risk of driving after drinking and alcohol-involved traffic accidents and deaths. Furthermore, the persistence of driving after drinking over time underscores a need for effective interventions to prevent future drunk driving in adulthood. The present study examined whether brief alcohol interventions (BAIs) for college students reduce driving after drinking.

Methods

A two-step meta-analysis of individual participant data (IPD) was conducted using a combined sample of 6801 college students from 15 randomized controlled trials (38% male, 72% White and 58% first-year students). BAIs included individually delivered Motivational Interviewing with Personalized Feedback (MI + PF), Group Motivational Interviewing (GMI), and stand-alone Personalized Feedback (PF) interventions. Two outcome variables, driving after two+/three+ drinks and driving after four+/five+ drinks, were checked, harmonized and analyzed separately for each study and then combined for meta-analysis and meta-regression analysis.

Results

BAIs lowered the risk of driving after four+/five+ drinks (19% difference in the odds of driving after drinking favoring BAIs vs. control), but not the risk of driving after two+/three+ drinks (9% difference). Subsequent subgroup analysis indicated that the MI + PF intervention was comparatively better than PF or GMI.

Conclusions

BAIs provide a harm reduction approach to college drinking. Hence, it is encouraging that BAIs reduce the risk of driving after heavy drinking among college students. However, there may be opportunities to enhance the intervention content and timing to be more relevant for driving after drinking and improve the outcome assessment and reporting to demonstrate its effect.

Harmonizing altered measures in integrative data analysis: A methods analogue study

In the current study, we used an analogue integrative data analysis (IDA) design to test optimal scoring strategies for harmonizing alcohol- and drug-use consequence measures with varying degrees of alteration across four study conditions. We evaluated performance of mean, confirmatory factor analysis (CFA), and moderated nonlinear factor analysis (MNLFA) scores based on traditional indices of reliability (test-retest, internal, and score recovery or parallel forms) and validity. Participants in the analogue study included 854 college students (46% male; 21% African American, 5% Hispanic/Latino, 56% European American) who completed two versions of the altered measures at two sessions, separated by 2 weeks. As expected, mean, CFA, and MNLFA scores all resulted in scales with lower reliability given increasing scale alteration (with less fidelity to formerly developed scales) and shorter scale length. MNLFA and CFA scores, however, showed greater validity than mean scores, demonstrating stronger relationships with external correlates. Implications for measurement harmonization in the context of IDA are discussed.

A comparison of Bayesian to maximum likelihood estimation for latent growth models in the presence of a binary outcome

Latent growth models (LGMs) are an application of structural equation modeling and frequently used in developmental and clinical research to analyze change over time in longitudinal outcomes. Maximum likelihood (ML), the most common approach for estimating LGMs, can fail to converge or may produce biased estimates in complex LGMs especially in studies with modest samples. Bayesian estimation is a logical alternative to ML for LGMs, but there is a lack of research providing guidance on when Bayesian estimation may be preferable to ML or vice versa. This study compared the performance of Bayesian versus ML estimators for LGMs by evaluating their accuracy via Monte Carlo (MC) simulations. For the MC study, longitudinal data sets were generated and estimated using LGM via both ML and Bayesian estimation with three different priors, and parameter recovery across the two estimators was evaluated to determine their relative performance. The findings suggest that ML estimation is a reasonable choice for most LGMs, unless it fails to converge, which can occur with limiting data situations (i.e., just a few time points, no covariate or outcome, modest sample sizes). When models do not converge using ML, we recommend Bayesian estimation with one caveat that the influence of the priors on estimation may have to be carefully examined, per recent recommendations on Bayesian modeling for applied researchers.

Methods for Population Research on Substance Use and Consequences

This chapter reviews the array of methods used in contemporary research on population-level research on substance use and its consequences. We argue that there are critical questions that can best - or in some cases, only - be addressed at the level of a population. We then describe the major categories of data collection methods used in population research, including surveys, ecological momentary assessment, administrative data, audit methods, and unobtrusive assessment of substance use. Two categories of measures are then discussed: measures of an individual's use of substances and related problems and measures of harm to others caused by one's use. We then review factors that may be considered causes or correlates of substance use and consequences, including both individual and environmental factors. We close with a few thoughts on the accumulation of knowledge and its translation to policy and practice.

A CD-based mapping method for combining multiple related parameters from heterogeneous intervention trials

Effect size can differ as a function of the elapsed time since treatment or as a function of other key covariates, such as sex or age. In evidence synthesis, a better understanding of the precise conditions under which treatment does work or does not work well has been highly valued. With increasingly accessible individual patient or participant data (IPD), more precise and informative inference can be within our reach. However, simultaneously combining multiple related parameters across heterogeneous studies is challenging because each parameter from each study has a specific interpretation within the context of the study and other covariates in the model. This paper proposes a novel mapping method to combine study-specific estimates of multiple related parameters across heterogeneous studies, which ensures valid inference at all inference levels by combining sample-dependent functions known as Confidence Distributions (CD). We describe the "CD-based mapping method" and provide a data application example for a multivariate random-effects meta-analysis model. We estimated up to 13 study-specific regression parameters for each of 14 individual studies using IPD in the first step, and subsequently combined the study-specific vectors of parameters, yielding a full vector of hyperparameters in the second step of meta-analysis. Sensitivity analysis indicated that the CD-based mapping method is robust to model misspecification. This novel approach to multi-parameter synthesis provides a reasonable methodological solution when combining complex evidence using IPD.

A Mobile Phone-Based Brief Intervention With Personalized Feedback and Text Messaging Is Associated With Reductions in Driving After Drinking Among College Drinkers

OBJECTIVE

Driving after drinking (DAD) among college students remains a significant public health concern and is perhaps the single riskiest drinking-related behavior. Counselor-delivered and web-based Brief Alcohol Interventions (BAIs) have been shown to reduce DAD among college students, but to date no study has evaluated the efficacy of a single-session mobile phone-based BAI specific to DAD. The present study examined whether a driving-specific BAI delivered via mobile phone would significantly decrease DAD among college students compared to an informational control.

METHOD

Participants were 84 college students (67.1% women; average age = 23; 52.4% White) who endorsed driving after drinking two or more drinks at least twice in the past 3 months. After completing baseline measures, participants were randomly assigned to receive either (a) DAD information or (b) DAD mobile BAI that included personalized feedback and interactive text messaging. Participants completed outcome measures at 3-month follow-up.

RESULTS

Repeated-measures mixed modeling analyses revealed that students receiving the mobile phone-based BAI reported significantly greater reductions in likelihood of DAD (three or more drinks) and the number of drinks consumed before driving than students in the information condition at 3-month follow-up.

CONCLUSIONS

These findings provide preliminary support for the short-term efficacy of a mobile phone-based BAI for reducing DAD among college students.

A tutorial on individual participant data meta-analysis using Bayesian multilevel modeling to estimate alcohol intervention effects across heterogeneous studies

This paper provides a tutorial companion for the methodological approach implemented in Huh et al. (2015) that overcame two major challenges for individual participant data (IPD) meta-analysis. Specifically, we show how to validly combine data from heterogeneous studies with varying numbers of treatment arms, and how to analyze highly-skewed count outcomes with many zeroes (e.g., alcohol and substance use outcomes) to estimate overall effect sizes. These issues have important implications for the feasibility, applicability, and interpretation of IPD meta-analysis but have received little attention thus far in the applied research literature. We present a Bayesian multilevel modeling approach for combining multi-arm trials (i.e., those with two or more treatment groups) in a distribution-appropriate IPD analysis. Illustrative data come from Project INTEGRATE, an IPD meta-analysis study of brief motivational interventions to reduce excessive alcohol use and related harm among college students. Our approach preserves the original random allocation within studies, combines within-study estimates across all studies, overcomes between-study heterogeneity in trial design (i.e., number of treatment arms) and/or study-level missing data, and derives two related treatment outcomes in a multivariate IPD meta-analysis. This methodological approach is a favorable alternative to collapsing or excluding intervention groups within multi-arm trials, making it possible to directly compare multiple treatment arms in a one-step IPD meta-analysis. To facilitate application of the method, we provide annotated computer code in R along with the example data used in this tutorial.

Multivariate Higher-Order IRT Model and MCMC Algorithm for Linking Individual Participant Data From Multiple Studies

Many clinical and psychological constructs are conceptualized to have multivariate higher-order constructs that give rise to multidimensional lower-order traits. Although recent measurement models and computing algorithms can accommodate item response data with a higher-order structure, there are few measurement models and computing techniques that can be employed in the context of complex research synthesis, such as meta-analysis of individual participant data or integrative data analysis. The current study was aimed at modeling complex item responses that can arise when underlying domain-specific, lower-order traits are hierarchically related to multiple higher-order traits for individual participant data from multiple studies. We formulated a multi-group, multivariate higher-order item response theory (HO-IRT) model from a Bayesian perspective and developed a new Markov chain Monte Carlo (MCMC) algorithm to simultaneously estimate the (a) structural parameters of the first- and second-order latent traits across multiple groups and (b) item parameters of the model. Results from a simulation study support the feasibility of the MCMC algorithm. From the analysis of real data, we found that a bivariate HO-IRT model with different correlation/covariance structures for different studies fit the data best, compared to a univariate HO-IRT model or other alternate models with unreasonable assumptions (i.e., the same means and covariances across studies). Although more work is needed to further develop the method and to disseminate it, the multi-group multivariate HO-IRT model holds promise to derive a common metric for individual participant data from multiple studies in research synthesis studies for robust inference and for new discoveries.

Preventing Youth Internalizing Symptoms Through the Familias Unidas Intervention: Examining Variation in Response

Prevention programs that strengthen parenting and family functioning have been found to reduce poor behavioral outcomes in adolescents, including substance use, HIV risk, externalizing and internalizing problems. However, there is evidence that not all youth benefit similarly from these programs. Familias Unidas is a family-focused intervention designed to prevent substance use and sexual risk among Hispanic youth and has recently demonstrated unanticipated reductions in internalizing symptoms for some youth. This paper examines variation in intervention response for internalizing symptoms using individual-level data pooled across four distinct Familias Unidas trials: (1) 266 eighth grade students recruited from the general school population; (2) 160 ninth grade students from the general school population; (3) 213 adolescents with conduct, aggression, and/or attention problems; and (4) 242 adolescents with a delinquency history. Causal inference growth mixture modeling suggests a three-class model. The two largest classes represent youth with low (60 %) and medium (27 %) internalizing symptoms at baseline, and both intervention and control participants show reductions in internalizing symptoms. The third class (13 %) represents youth with high levels of baseline internalizing symptoms who remain at steady levels of internalizing symptoms when exposed to the intervention, but who experience an increase in symptoms under the control condition. Female gender, low baseline levels of parent-adolescent communication, and older age were associated with membership in the high-risk class. These synthesis analyses involving a large sample of youth with varying initial risk levels represent a further step toward strengthening our knowledge of preventive intervention response and improving preventive interventions.

Brief Motivational Interventions Are Associated with Reductions in Alcohol-Induced Blackouts Among Heavy Drinking College Students

BACKGROUND

Alcohol-induced blackouts, a form of anterograde amnesia that restricts the encoding of short-term memories into long-term ones, are among the most severe alcohol-related consequences. College students are at high risk of experiencing alcohol-induced blackouts, and there is a need to determine whether alcohol interventions can effectively reduce blackouts in this population. The current study uses data from 3 randomized clinical trials to examine the effect of various intervention approaches on alcohol-induced blackouts.

METHODS

Four interventions were compared over 3 studies: (i) a computerized feedback intervention (electronic Check-Up To Go [e-Chug]; Study 1); (ii) a single-session brief motivational intervention (BMI; Study 1); (iii) a BMI plus behavioral economic session focused on increasing substance-free activities (BMI + Substance-Free Activity Session [SFAS]; Studies 2 and 3); and (iv) a BMI plus supplemental Relaxation Training session (BMI + Relaxation Training; Studies 2 and 3). Studies 1 and 3 also included an assessment-only control condition. For each study, participants reported whether they had experienced an alcohol-induced blackout at each time point; binary logistic regressions examined differential likelihood of experiencing an alcohol-induced blackout over time.

RESULTS

Neither the single-session BMI nor e-Chug reduced alcohol-induced blackouts over assessment only; however, participants in the BMI + SFAS or BMI + Relaxation Training condition were significantly less likely to experience an alcohol-induced blackout compared to assessment only at 1-month (Wald = 4.77, odds ratio [OR] = 0.53, p = 0.03) and 6-month follow-ups (Wald = 5.72, OR = 0.52, p = 0.02). Study 2 also revealed a larger effect for the BMI + SFAS over the BMI + Relaxation Training condition at 6 months (Wald = 4.11 OR = 0.22, p = 0.043), although this was not replicated in Study 3. The effects for the 2-session BMIs lasted 6 months, at which point maturation effects diminished differences between assessment-only and intervention conditions.

CONCLUSIONS

Two sessions of BMI are a substantial enough dose to result in reductions in alcohol-induced blackouts among college student heavy drinkers.

A Mobile Phone-Based Brief Intervention With Personalized Feedback and Text Messaging Is Associated With Reductions in Driving After Drinking Among College Drinkers

OBJECTIVE

Driving after drinking (DAD) among college students remains a significant public health concern and is perhaps the single riskiest drinking-related behavior. Counselor-delivered and web-based Brief Alcohol Interventions (BAIs) have been shown to reduce DAD among college students, but to date no study has evaluated the efficacy of a single-session mobile phone-based BAI specific to DAD. The present study examined whether a driving-specific BAI delivered via mobile phone would significantly decrease DAD among college students compared to an informational control.

METHOD

Participants were 84 college students (67.1% women; average age = 23; 52.4% White) who endorsed driving after drinking two or more drinks at least twice in the past 3 months. After completing baseline measures, participants were randomly assigned to receive either (a) DAD information or (b) DAD mobile BAI that included personalized feedback and interactive text messaging. Participants completed outcome measures at 3-month follow-up.

RESULTS

Repeated-measures mixed modeling analyses revealed that students receiving the mobile phone-based BAI reported significantly greater reductions in likelihood of DAD (three or more drinks) and the number of drinks consumed before driving than students in the information condition at 3-month follow-up.

CONCLUSIONS

These findings provide preliminary support for the short-term efficacy of a mobile phone-based BAI for reducing DAD among college students.

Advancing the Study of Adolescent Substance Use Through the Use of Integrative Data Analysis

A wealth of information is currently known about the epidemiology, etiology, and evaluation of drug and alcohol use across the life span. Despite this corpus of knowledge, much has yet to be learned. Many factors conspire to slow the pace of future advances in the field of substance use including the need for long-term longitudinal studies of often hard-to-reach subjects who are reporting rare and episodic behaviors. One promising option that might help move the field forward is integrative data analysis (IDA). IDA is a principled set of methodologies and statistical techniques that allow for the fitting of statistical models to data that have been pooled across multiple, independent samples. IDA offers a myriad of potential advantages including increased power, greater coverage of rare behaviors, more rigorous psychometric assessment of theoretical constructs, accelerated developmental time period under study, and enhanced reproducibility. However, IDA is not without limitations and may not be useful in a given application for a variety of reasons. The goal of this article is to describe the advantages and limitations of IDA in the study of individual development over time, particularly as it relates to trajectories of substance use. An empirical example of the measurement of polysubstance use is presented and this article concludes with recommendations for practice.

An Algorithm for Creating Virtual Controls Using Integrated and Harmonized Longitudinal Data

We introduce a strategy for creating virtual control groups-cases generated through computer algorithms that, when aggregated, may serve as experimental comparators where live controls are difficult to recruit, such as when programs are widely disseminated and randomization is not feasible. We integrated and harmonized data from eight archived longitudinal adolescent-focused data sets spanning the decades from 1980 to 2010. Collectively, these studies examined numerous psychosocial variables and assessed past 30-day alcohol, cigarette, and marijuana use. Additional treatment and control group data from two archived randomized control trials were used to test the virtual control algorithm. Both randomized controlled trials (RCTs) assessed intentions, normative beliefs, and values as well as past 30-day alcohol, cigarette, and marijuana use. We developed an algorithm that used percentile scores from the integrated data set to create age- and gender-specific latent psychosocial scores. The algorithm matched treatment case observed psychosocial scores at pretest to create a virtual control case that figuratively "matured" based on age-related changes, holding the virtual case's percentile constant. Virtual controls matched treatment case occurrence, eliminating differential attrition as a threat to validity. Virtual case substance use was estimated from the virtual case's latent psychosocial score using logistic regression coefficients derived from analyzing the treatment group. Averaging across virtual cases created group estimates of prevalence. Two criteria were established to evaluate the adequacy of virtual control cases: (1) virtual control group pretest drug prevalence rates should match those of the treatment group and (2) virtual control group patterns of drug prevalence over time should match live controls. The algorithm successfully matched pretest prevalence for both RCTs. Increases in prevalence were observed, although there were discrepancies between live and virtual control outcomes. This study provides an initial framework for creating virtual controls using a step-by-step procedure that can now be revised and validated using other prevention trial data.

Emotion Regulation Enhancement of Cognitive Behavior Therapy for College Student Problem Drinkers: A Pilot Randomized Controlled Trial

This pilot randomized clinical trial tested an emotion regulation enhancement to Cognitive Behavior Therapy (CBT) with 29 college student problem drinkers with histories of complex trauma and current clinically significant traumatic stress symptoms. Participants received eight face-to-face sessions of manualized internet-supported CBT for problem drinking with or without trauma-focused emotion regulation skills (Trauma Affect Regulation: Guide for Education and Therapy, TARGET). Both interventions were associated with sustained (at one-month follow-up) reductions in self-reported drinking frequency, drinking related impairment, and heavy drinking in the past week, as well as post-traumatic stress disorder (PTSD) and complex PTSD symptoms, and improvement in self-reported emotion regulation. The enhanced intervention was associated with significantly greater sustained reductions in complex PTSD symptoms and resulted in medium/large effect size reductions in days of alcohol use (versus small effects by CBT). Emotion regulation enhancement of CBT for college student problem drinkers with interpersonal trauma histories warrants further investigation.

Depressive symptom domains and alcohol use severity among Hispanic emerging adults: Examining moderating effects of gender

OBJECTIVE

A limited amount of research has examined the effects of unique depressive symptom domains on alcohol use behavior among Hispanics of any developmental stage. This study aimed to (a) examine the respective associations between depressive symptom domains (e.g., negative affect, anhedonia, interpersonal problems, and somatic complaints) and alcohol use severity among Hispanic emerging adults, and (b) examine if gender moderates each respective association.

METHOD

181 Hispanic emerging adults (ages 18-25) completed an anonymous cross-sectional online survey. Participants completed a demographic questionnaire, the Alcohol Use Disorder Identification Test, and the Center Epidemiological Studies Depression Scale. Hierarchical multiple regression was used to estimate respective associations of negative affect, anhedonia, interpersonal problems, and somatic complaints in relation to alcohol use severity. Moderation tests were also conducted to examine if gender functioned as an effect modifier between respective depressive symptom domains and alcohol use severity.

RESULTS

Findings indicated higher levels of anhedonia were associated with higher alcohol use severity (β=0.20, p=0.02). Moderation analyses indicated that somatic complaints (β=-0.41, p=0.02) and interpersonal problems were associated with greater alcohol use severity among men (β=-0.60, p<0.001), but not women.

CONCLUSIONS

Findings underscore the need to examine the relationship between specific depressive symptom domains and alcohol use; and the importance of accounting for potential gender differences in these associations.

Alcohol Demand, Future Orientation, and Craving Mediate the Relation Between Depressive and Stress Symptoms and Alcohol Problems

BACKGROUND

Elevated depression and stress have been linked to greater levels of alcohol problems among young adults even after taking into account drinking level. This study attempts to elucidate variables that might mediate the relation between symptoms of depression and stress and alcohol problems, including alcohol demand, future time orientation, and craving.

METHODS

Participants were 393 undergraduates (60.8% female, 78.9% White/Caucasian) who reported at least 2 binge-drinking episodes (4/5+ drinks for women/men, respectively) in the previous month. Participants completed self-report measures of stress and depression, alcohol demand, future time orientation, craving, and alcohol problems.

RESULTS

In separate mediation models that accounted for gender, race, and weekly alcohol consumption, future orientation and craving significantly mediated the relation between depressive symptoms and alcohol problems. Alcohol demand, future orientation, and craving significantly mediated the relation between stress symptoms and alcohol problems.

CONCLUSIONS

Heavy-drinking young adults who experience stress or depression are likely to experience alcohol problems, and this is due in part to elevations in craving and alcohol demand, and less sensitivity to future outcomes. Interventions targeting alcohol misuse in young adults with elevated levels of depression and stress should attempt to increase future orientation and decrease craving and alcohol reward value.

Do drinking motives distinguish extreme drinking college students from their peers?

OBJECTIVE

The literature highlights the need to move beyond the traditional heavy episodic ("binge") drinking criteria when trying to identify at-risk college drinkers. Thus, recent attention has focused on more extreme levels of drinking. This study examines whether drinking motives can distinguish college student extreme drinkers from lighter drinkers.

METHOD

We used data from 3518 college student current drinkers (63.4% women) who participated in eight different studies at five different college campuses across the United States; a subsample of these students was followed up at 6months post-baseline. At baseline and follow-up, drinkers were divided into three groups: nonbinge drinkers (<4 drinks for women and 5 for men on their maximum drinking occasion), binge drinkers (4-7 drinks for women; 5-9 for men), and extreme drinkers (8+ for women and 10+ for men).

RESULTS

At baseline, extreme drinkers, compared to nonbinge and binge drinkers, reported greater social, enhancement, and coping motives, as well as greater quantity and frequency of drinking per week and more alcohol-related problems. Those who were not extreme drinkers at baseline and later became extreme drinkers at follow-up reported significantly greater increases in social and enhancement motives, compared to those who remained nonextreme drinkers. Those who were extreme drinkers at baseline and reduced their drinking 6months later, compared to those who remained extreme drinkers, reported greater reductions in enhancement and coping motives.

CONCLUSIONS

Focusing on drinking motives might be an efficacious target for preventive intervention programs to reduce extreme drinking among college students.

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

BACKGROUND

Cochrane recently published a systematic review on motivational interviewing (MI) for alcohol misuse in young adults. The review authors concluded that 'there are no substantive, meaningful benefits of MI interventions for the prevention of alcohol misuse' (p. 2), as effect sizes were 'small and unlikely to be of any meaningful benefit in practice' (p. 27). As most of these interventions were quite brief, we wish to open a dialogue about interpreting effect sizes in this review and of (brief) alcohol interventions more generally.

ANALYSIS

We analyze four methodological aspects of the review that likely influenced the author's conclusions about intervention effects: (1) risk of bias assessments, (2) search strategies, (3) assessing the quality of the body of evidence and (4) definitions of sustainability and clinical significance.

CONCLUSIONS

We interpret the effect sizes found in this review to indicate modest yet beneficial and potentially meaningful effects of these interventions, given their brevity and low cost. This interpretation is consistent with other reviews on brief, MI-based interventions and brief interventions more generally. We therefore encourage the field to re-open dialogue about the clinical importance of the effects of MI on alcohol misuse by young adults. Rather than dismissing interventions with small effects, we believe a more fruitful way forward for the field would be to catalogue effect sizes for various alcohol interventions. Such a catalogue would help stakeholders themselves to choose which interventions meet their minimum desired impact, and thus may be suitable given their targeted populations, setting and resources.

Brief motivational interventions for college student drinking may not be as powerful as we think: an individual participant-level data meta-analysis

BACKGROUND

For over 2 decades, brief motivational interventions (BMIs) have been implemented on college campuses to reduce heavy drinking and related negative consequences. Such interventions include in-person motivational interviews (MIs), often incorporating personalized feedback (PF), and stand-alone PF interventions delivered via mail, computer, or the Web. Both narrative and meta-analytic reviews using aggregate data from published studies suggest at least short-term efficacy of BMIs, although overall effect sizes have been small.

METHODS

This study was an individual participant-level data (IPD) meta-analysis of 17 randomized clinical trials evaluating BMIs. Unlike typical meta-analysis based on summary data, IPD meta-analysis allows for an analysis that correctly accommodates the sampling, sample characteristics, and distributions of the pooled data. In particular, highly skewed distributions with many zeroes are typical for drinking outcomes, but have not been adequately accounted for in existing studies. Data are from Project INTEGRATE, one of the largest IPD meta-analysis projects to date in alcohol intervention research, representing 6,713 individuals each with 2 to 5 repeated measures up to 12 months postbaseline.

RESULTS

We used Bayesian multilevel over dispersed Poisson hurdle models to estimate intervention effects on drinks per week and peak drinking, and Gaussian models for alcohol problems. Estimates of overall intervention effects were very small and not statistically significant for any of the outcomes. We further conducted post hoc comparisons of 3 intervention types (individual MI with PF, PF only, and group MI) versus control. There was a small, statistically significant reduction in alcohol problems among participants who received an individual MI with PF. Short-term and long-term results were similar.

CONCLUSIONS

This study questions the efficacy and magnitude of effects of BMIs for college drinking prevention and intervention and suggests a need for the development of more effective intervention strategies.

Brief Motivational Interventions Are Associated With Reductions in Alcohol-Impaired Driving Among College Drinkers

OBJECTIVE

Alcohol-impaired (AI) driving among college students remains a significant public health concern and may be the single most risky drinking outcome among young adults. Brief motivational interventions (BMIs) have been shown to reduce alcohol use and problems, but their specific efficacy for decreasing AI driving among college students is unknown. The present study analyzed data from three randomized controlled trials of BMI (Murphy et al., 2010: n = 74; Borsari et al., 2012: n = 530; and Martens et al., 2013: n = 365) to evaluate whether BMIs are associated with reductions in AI driving among college student drinkers.

METHOD

Participants in all three studies were randomized to BMI or control conditions. Participants reported whether they had driven under the influence (yes/no) following the BMI over the follow-up period.

RESULTS

Separate binary logistic regression analyses were conducted for each study. For Studies 1 and 2, these analyses revealed that a BMI was significantly associated with reductions in AI driving at the final (6-month and 9-month, respectively) follow-up compared with the control condition. For Study 3, analyses revealed that a single-component BMI focused on the correction of misperceptions of descriptive norms was significantly associated with reductions in AI driving compared with the control group at the final (6-month) followup, whereas a single-component BMI focused on the use of protective behavioral strategies was not. Change in drinking level did not mediate the relationship between the condition and the change in AI driving.

CONCLUSIONS

Counselor-administered BMIs that include descriptive normative feedback are associated with significant reductions in AI driving compared with control.

Are there secondary effects on marijuana use from brief alcohol interventions for college students?

OBJECTIVE

This study examined whether brief motivational interventions (BMIs) designed for reducing heavy drinking among college students have secondary effects on reducing marijuana use.

METHOD

The data came from Project INTEGRATE, which combined data from 24 independent trials of BMIs and other individual-focused interventions designed to reduce heavy drinking and related problems among college students. We analyzed data from 10 samples across nine studies that used random assignment of participants into either a BMI or a control group and assessed marijuana use outcomes (N = 6,768; 41.5% men; 73.2% White; 57.7% first-year students; 19.2% current marijuana users at baseline). We derived three marijuana use groups within studies by cross-tabulating baseline and follow-up data: Nonusers, Reducers, and Stayers/Increasers.

RESULTS

Peto's one-step odds ratio analyses for meta-analysis revealed no significant intervention effects on marijuana use at either short-term (1-3 month) or long-term (6-12 month) follow-up. Subsequent exploratory analyses showed that those who reduced drinking were more likely to be a marijuana Reducer or Nonuser, compared with a Stayer/Increaser, at both follow-ups.

CONCLUSIONS

The BMIs to reduce heavy drinking evaluated in this study did not reduce marijuana use. However, our exploratory results suggest that if we can develop interventions for college students that effectively reduce drinking, we may also reduce their marijuana use. Furthermore, as recreational use of marijuana becomes legal or decriminalized and marijuana becomes more readily available, it may be necessary to develop interventions specifically targeting marijuana use among college students.

Reproducibility and differential item functioning of the alcohol dependence syndrome construct across four alcohol treatment studies: An integrative data analysis

BACKGROUND

The validity of the alcohol dependence syndrome has been supported. The question of whether different measures of the construct are comparable across studies and patient subgroups has not been examined. This study examined the alcohol dependence construct across four diverse large-scale treatment samples using integrative data analysis (IDA).

METHOD

We utilized existing data (n=4393) from the COMBINE Study, Project MATCH, the Relapse Replication and Extension Project (RREP), and the United Kingdom Alcohol Treatment Trial (UKATT). We focused on four measures of alcohol dependence: the Alcohol Dependence Scale (COMBINE and RREP), Alcohol Use Inventory (MATCH), the Leeds Dependence Questionnaire (UKATT), and the Diagnostic and Statistical Manual of Mental Disorders (COMBINE and MATCH). Moderated nonlinear factor analysis was used to create a measure of alcohol dependence severity that was moderated by study membership, gender, age, and marital status.

RESULTS

A commensurate measure of alcohol dependence severity was successfully created using 20 items available in four studies. We identified differential item functioning by study membership, age, gender, and/or marital status for 12 of the 20 items, indicating specific patient subgroups who responded differently to items based on their underlying dependence severity.

CONCLUSIONS

Alcohol dependence severity is a single unidimensional construct that is comparable across studies. The use of IDA provided a strong test of the validity of the alcohol dependence syndrome and clues as to how some items used to measure dependence severity may be more or less central to the construct for some patients.

Trajectories of binge drinking and personality change across emerging adulthood

College students binge drink more frequently than the broader population, yet most individuals "mature out" of binge drinking. Impulsivity and sensation seeking traits are important for understanding who is at risk for maintaining binge drinking across college and the transition to adult roles. We use latent class growth analysis (LCGA) to examine longitudinal binge-drinking trajectories spanning from the end of high school through 2 years after college (M ages = 18.4 to 23.8). Data were gathered over 10 waves from students at a large Southwestern university (N = 2,245). We use latent factor models to estimate changes in self-reported impulsive (IMP) and sensation-seeking (SS) personality traits across 2 time periods-(a) the end of high school to the end of college and (b) the 2-year transition out of college. LCGA suggested 7 binge-drinking trajectories: frequent, moderate, increasing, occasional, low increasing, decreasing, and rare. Models of personality showed that from high school through college, change in SS and IMP generally paralleled drinking trajectories, with increasing and decreasing individuals showing corresponding changes in SS. Across the transition out of college, only the increasing group demonstrated a developmentally deviant increase in IMP, whereas all other groups showed normative stability or decreases in both IMP and SS. These data indicate that "late bloomers," who begin binge drinking only in the later years of college, are a unique at-risk group for drinking associated with abnormal patterns of personality maturation during emerging adulthood. Our results indicate that personality targeted interventions may benefit college students.

Trajectories of binge drinking and personality change across emerging adulthood

College students binge drink more frequently than the broader population, yet most individuals "mature out" of binge drinking. Impulsivity and sensation seeking traits are important for understanding who is at risk for maintaining binge drinking across college and the transition to adult roles. We use latent class growth analysis (LCGA) to examine longitudinal binge-drinking trajectories spanning from the end of high school through 2 years after college (M ages = 18.4 to 23.8). Data were gathered over 10 waves from students at a large Southwestern university (N = 2,245). We use latent factor models to estimate changes in self-reported impulsive (IMP) and sensation-seeking (SS) personality traits across 2 time periods-(a) the end of high school to the end of college and (b) the 2-year transition out of college. LCGA suggested 7 binge-drinking trajectories: frequent, moderate, increasing, occasional, low increasing, decreasing, and rare. Models of personality showed that from high school through college, change in SS and IMP generally paralleled drinking trajectories, with increasing and decreasing individuals showing corresponding changes in SS. Across the transition out of college, only the increasing group demonstrated a developmentally deviant increase in IMP, whereas all other groups showed normative stability or decreases in both IMP and SS. These data indicate that "late bloomers," who begin binge drinking only in the later years of college, are a unique at-risk group for drinking associated with abnormal patterns of personality maturation during emerging adulthood. Our results indicate that personality targeted interventions may benefit college students.

Recommendations for the Design and Analysis of Treatment Trials for Alcohol Use Disorders

BACKGROUND

Over the past 60 years, the view that "alcoholism" is a disease for which the only acceptable goal of treatment is abstinence has given way to the recognition that alcohol use disorders (AUDs) occur on a continuum of severity, for which a variety of treatment options are appropriate. However, because the available treatments for AUDs are not effective for everyone, more research is needed to develop novel and more efficacious treatments to address the range of AUD severity in diverse populations. Here we offer recommendations for the design and analysis of alcohol treatment trials, with a specific focus on the careful conduct of randomized clinical trials of medications and nonpharmacological interventions for AUDs.

METHODS

This paper provides a narrative review of the quality of published clinical trials and recommendations for the optimal design and analysis of treatment trials for AUDs.

RESULTS

Despite considerable improvements in the design of alcohol clinical trials over the past 2 decades, many studies of AUD treatments have used faulty design features and statistical methods that are known to produce biased estimates of treatment efficacy.

CONCLUSIONS

The published statistical and methodological literatures provide clear guidance on methods to improve clinical trial design and analysis. Consistent use of state-of-the-art design features and analytic approaches will enhance the internal and external validity of treatment trials for AUDs across the spectrum of severity. The ultimate result of this attention to methodological rigor is that better treatment options will be identified for patients with an AUD.