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.

a b s t r a c tBackground: The use of amphetamines is a global public health concern. We summarise global data on use of amphetamines and mental health outcomes. Methods: A systematic review and meta-analysis (CRD 42017081893). We searched Medline, EMBASE, PsycInfo for methamphetamine or amphetamine combined with psychosis, violence, suicidality, depression or anxiety. Included studies were human empirical cross-sectional surveys, case-control studies, cohort studies and randomised controlled trials that assessed the association between methamphetamine and one of the mental health outcomes. Random effects meta-analysis was used to pool results for any use of amphetamines and amphetamine use disorders. Findings: 149 studies were eligible and 59 were included in meta-analyses. There was significant heterogeneity in effects. Evidence came mostly from cross-sectional studies. Any use of amphetamines was associated with higher odds of psychosis (odds ratio [OR] = 2.0, 95%CI 1.3-3.3), violence (OR = 2.2, 95%CI 1.2-4.1; adjusted OR [AOR] = 1.4, 95%CI 0.8-2.4), suicidality OR = 4.4, 95%CI 2.4-8.2; AOR = 1.7, 95%CI 1.0-2.9) and depression (OR = 1.6, 95%CI 1.1-2.2; AOR = 1.3, 95%CI 1.2-1.4). Having an amphetamine use disorder was associated with higher odds of psychosis (OR = 3.0, 95%CI 1.9-4.8; AOR = 2.4, 95%CI 1.6-3.5), violence (OR = 6.2, 95%CI 3.1-12.3), and suicidality (OR = 2.3, 95%CI 1.8-2.9; AOR = 1.5, 95%CI 1.3-1.8). Interpretation: Methamphetamine use is an important risk factor for poor mental health. High quality population-level studies are needed to more accurately quantify this risk. Clinical responses to methamphetamine use need to address mental health harms.

There is consistent evidence that mood disorders often co-occur with anxiety disorders, however, the strength of the association of these two broad groups of disorders has been challenging to summarize across different studies. The aim was to conduct a metaanalysis of publications reporting on the pairwise comorbidity between mood and anxiety disorders after sorting into comparable study types. We searched MEDLINE, Embase, CINAHL, Web of Science, and the grey literature for publications between 1980 and 2017 regardless of geographical locations and languages. We meta-analyzed estimates from original articles after sorting by: (a) broad or narrow diagnostic criteria, (b) study time-frame, and (c) estimates with or without covariate adjustments. Over 43 000 unique studies were identified through electronic searches, of which 391 were selected for fulltext review. Finally, 171 studies were eligible for inclusion, including 53 articles from additional snowball searching. In general, regardless of variations in diagnosis type, study time-frame, temporal order, or use of adjustments, there was substantial comorbidity between mood and anxiety disorders. Based on the entire 90 separate meta-analyses, the median OR was 6.1 (range 1.5-18.7). Of these estimates, all 90 were above 1, and 87 were significantly greater than 1 (i.e., the 95% confidence intervals did not include 1). Fourteen of the 90 pooled estimates had ORs that were greater than 10. This systematic review found robust and consistent evidence of comorbidity between broadly defined mood and anxiety disorders. Clinicians should be vigilant for the prompt identification and treatment of this common type of comorbidity.

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The present paper proposes a hierarchical, multi-unidimensional two-parameter logistic item response theory (2PL-MUIRT) model extended for a large number of groups. The proposed model was motivated by a large-scale integrative data analysis (IDA) study which combined data (N = 24,336) from 24 independent alcohol intervention studies. IDA projects face unique challenges that are different from those encountered in individual studies, such as the need to establish a common scoring metric across studies and to handle missingness in the pooled data. To address these challenges, we developed a Markov chain Monte Carlo (MCMC) algorithm for a hierarchical 2PL-MUIRT model for multiple groups in which not only were the item parameters and latent traits estimated, but the means and covariance structures for multiple dimensions were also estimated across different groups. Compared to a few existing MCMC algorithms for multidimensional IRT models that constrain the item parameters to facilitate estimation of the covariance matrix, we adapted an MCMC algorithm so that we could directly estimate the correlation matrix for the anchor group without any constraints on the item parameters. The feasibility of the MCMC algorithm and the validity of the basic calibration procedure were examined using a simulation study. Results showed that model parameters could be adequately recovered, and estimated latent trait scores closely approximated true latent trait scores. The algorithm was then applied to analyze real data (69 items across 20 studies for 22,608 participants). The posterior predictive model check showed that the model fit all items well, and the correlations between the MCMC scores and original scores were overall quite high. An additional simulation study demonstrated robustness of the MCMC procedures in the context of the high proportion of missingness in data. The Bayesian hierarchical IRT model using the MCMC algorithms developed in the current study has the potential to be widely implemented for IDA studies or multi-site studies, and can be further refined to meet more complicated needs in applied research.