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

Posttraumatic stress and depression symptom classes in parents of trauma-exposed children: a transdiagnostic perspective using pooled individual participant data

Background: In the aftermath of child trauma, post-traumatic stress (PTS) and depression symptoms often co-occur among trauma exposed children and their parents. Studies have used latent class analysis (LCA) to examine PTS and depression symptoms and identify homogeneous subgroups among trauma exposed children. However, little is known about subgroups or classes of PTS and depression reactions of parents of traumatised children.Objectives: (1) Determine PTS and depression symptom classes at 2-9 months post-trauma, and (2) to examine sociodemographic covariates among parents of trauma exposed children.Methods: Using harmonised individual participant data (n = 702) from eight studies (Australia, UK, US) included in the Prospective studies of Acute Child Trauma and Recovery Data Archive (PACT/R), we modelled these phenomena at the symptom level using LCA.Results: Our LCA yielded three solutions: 'high internalizing symptom' class (11%); 'low PTS-high depression' class (17%); and 'low internalizing symptom' class (72%). Parents of children in the 'low PTS-high depression' class were more likely to have children of older age and be part of an ethnic minority, compared to the 'low internalizing symptoms' class. Mothers were more likely to be in the 'high internalizing symptom' class compared to the 'low internalizing symptoms' class.Conclusions: These findings reveal a qualitative structure and relationship between depression and PTS symptoms that highlights the importance of assessing and targeting a broad range of internalising symptoms in post-trauma psychological treatment.

Retrospective Psychometrics and Effect Heterogeneity in Integrated Data Analysis: Commentary on the Special Issue

The current special issue of Prevention Science indicates that momentum in using individual participant data (IPD) and integrative data analysis (IDA) to combine and synthesize findings in prevention science has accelerated over the past decade. In this commentary, we focus on two general themes involving methods for harmonizing measures and findings of effect heterogeneity. We describe methods for harmonization as retrospective psychometrics, requiring that we attend to the assumptions necessary for accurate measurement, but adjust our methods given the constraints of working with existing datasets that often involve different measures in different studies. We point to novel approaches for increasing confidence that semantic matching and empirical modeling used in these studies will yield accurate and valid measurements that can be combined in IDA. We also review findings about effect heterogeneity, emphasizing the importance of using etiologic and action theories to identify and evaluate sources of such effects. We note that all of the papers in this issue deserve careful attention, as they illustrate how prevention scientists are approaching the complexities of IDA and exploring novel methods for overcoming its challenges.

Advancing Intervention and Prevention Research for Behavioral Health Problems Through Data Synthesis

This commentary on the special issue, "Innovations and Applications of Integrative Data Analysis (IDA) and Related Data Harmonization Procedures in Prevention Science" summarizes the utility of data synthesis techniques to elucidate prevention effects in important ways, including effects on low base-rate conditions and effects across multiple small-scale studies of preventive interventions, long-term and crossover effects of preventive interventions, and addressing for whom preventive interventions work, and for how long. In addition, articles tackle methodological challenges by integrating and harmonizing data. Much progress has occurred in the past 5 years. We consider in this commentary the full collection of papers in the special issue, and their ongoing contributions of data synthesis methods for advancing research on the prevention of mental, emotional, and behavioral health problems. We organize our observations by several themes noted across the papers. We also highlight the National Institute of Mental Health's investments that align with many of the efforts summarized here to advance our understanding of prevention research.

Introduction to the Special Issue on Innovations and Applications of Integrative Data Analysis (IDA) and Related Data Harmonization Procedures in Prevention Science

This paper serves as an introduction to the special issue of Prevention Science entitled, "Innovations and Applications of Integrative Data Analysis (IDA) and Related Data Harmonization Procedures in Prevention Science." This special issue includes a collection of original papers from multiple disciplines that apply individual-level data synthesis methodologies, including IDA, individual participant meta-analysis, and other related methods to harmonize and integrate multiple datasets from intervention trials of the same or similar interventions. This work builds on a series of papers appearing in a prior Prevention Science special issue, entitled "Who Benefits from Programs to Prevent Adolescent Depression?" (Howe, Pantin, & Perrino, 2018). Since the publication of this prior work, the use of individual-level data synthesis has increased considerably in and outside of prevention. As such, there is a need for an update on current and future directions in IDA, with careful consideration of innovations and applications of these methods to fill important research gaps in prevention science. The papers in this issue are organized into two broad categories of (1) evidence synthesis papers that apply best practices in data harmonization and individual-level data synthesis and (2) new and emerging design, psychometric, and methodological issues and solutions. This collection of original papers is followed by two invited commentaries which provide insight and important reflections on the field and future directions for prevention science.

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.