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Prevention science has increasingly turned to integrative data analysis (IDA) to combine individual participant-level data from multiple studies of the same topic, allowing us to evaluate overall effect size, test and model heterogeneity, and examine mediation. Studies included in IDA often use different measures for the same construct, leading to sparse datasets. We introduce a graph theory method for summarizing patterns of sparseness and use simulations to explore the impact of different patterns on measurement bias within three different measurement models: a single common factor, a hierarchical model, and a bifactor model. We simulated 1000 datasets with varying levels of sparseness and used Bayesian methods to estimate model parameters and evaluate bias. Results clarified that bias due to sparseness will depend on the strength of the general factor, the measurement model employed, and the level of indirect linkage among measures. We provide an example using a synthesis dataset that combined data on youth depression from 4146 youth who participated in 16 randomized field trials of prevention programs. Given that different synthesis datasets will embody different patterns of sparseness, we conclude by recommending that investigators use simulation methods to explore the potential for bias given the sparseness patterns they encounter.

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This paper aims to elucidate the differentially coexpressed genes, their potential mechanisms, and possible drug targets in low-grade invasive serous ovarian carcinoma (LGSC) in terms of the biologic continuity of normal, borderline, and malignant LGSC. We performed a bioinformatics analysis, integrating datasets generated using the GPL570 platform from different studies from the GEO database to identify changes in this transition, gene expression, drug targets, and their relationships with tumor microenvironmental characteristics. In the transition from ovarian epithelial cells to the serous borderline, the FGFR3 gene in the "Estrogen Response Late" pathway, the ITGB2 gene in the "Cell Adhesion Molecule", the CD74 gene in the "Regulation of Cell Migration", and the IGF1 gene in the "Xenobiotic Metabolism" pathway were upregulated in the transition from borderline to LGSC. The ERBB4 gene in "Proteoglycan in Cancer", the AR gene in "Pathways in Cancer" and "Estrogen Response Early" pathways, were upregulated in the transition from ovarian epithelial cells to LGSC. In addition, SPP1 and ITGB2 genes were correlated with macrophage infiltration in the LGSC group. This research provides a valuable framework for the development of personalized therapeutic approaches in the context of LGSC, with the aim of improving patient outcomes and quality of life. Furthermore, the main goal of the current study is a preliminary study designed to generate in silico inferences, and it is also important to note that subsequent in vitro and in vivo studies will be necessary to confirm the results before considering these results as fully reliable.

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Researchers of aggression have classically focused on what has been previously called active aggression-the deliberate infliction of harm through the direct application of deleterious consequences. However, the counterpart to this, what was originally called passive aggression, has gone understudied, and its definition has mutated beyond its original conceptualization. The present two studies (N's 196 and 220, respectively) attempted to examine passive aggression as originally defined-the deliberate withholding of behavior to ensure that a target is harmed-and renaming it aggression by omission (ABO), in contrast to aggression by commission (ABC). These studies found that both fit within a similar nomological network of antagonism, Sadism, and trait aggression. Study 2 additionally found that both were equally affected by provocation and were considered equally harmful. These findings encourage further research into ABO to capture this construct concretely, especially in the context of common paradigms (e.g., the Taylor Aggression Paradigm, Hot Sauce, Point-Subtraction Aggression Paradigm), and trait aggression scales, which typically measure ABC.

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BACKGROUND: Multi-omics research has the potential to holistically capture intra-tumor variability, thereby improving therapeutic decisions by incorporating the key principles of precision medicine. The purpose of this study is to identify a robust method of integrating features from different sources, such as imaging, transcriptomics, and clinical data, to predict the survival and therapy response of non-small cell lung cancer patients. METHODS: 2996 radiomics, 5268 transcriptomics, and 8 clinical features were extracted from the NSCLC Radiogenomics dataset. Radiomics and deep features were calculated based on the volume of interest in pre-treatment, routine CT examinations, and then combined with RNA-seq and clinical data. Several machine learning classifiers were used to perform survival analysis and assess the patient's response to adjuvant chemotherapy. The proposed analysis was evaluated on an unseen testing set in a k-fold cross-validation scheme. Score- and concatenation-based multi-omics were used as feature integration techniques. RESULTS: Six radiomics (elongation, cluster shade, entropy, variance, gray-level non-uniformity, and maximal correlation coefficient), six deep features (NasNet-based activations), and three transcriptomics (OTUD3, SUCGL2, and RQCD1) were found to be significant for therapy response. The examined score-based multi-omic improved the AUC up to 0.10 on the unseen testing set (0.74 ± 0.06) and the balance between sensitivity and specificity for predicting therapy response for 106 patients, resulting in less biased models and improving upon the either highly sensitive or highly specific single-source models. Six radiomics (kurtosis, GLRLM- and GLSZM-based non-uniformity from images with no filtering, biorthogonal, and daubechies wavelets), seven deep features (ResNet-based activations), and seven transcriptomics (ELP3, ZZZ3, PGRMC2, TRAK1, ATIC, USP7, and PNPLA2) were found to be significant for the survival analysis. Accordingly, the survival analysis for 115 patients was also enhanced up to 0.20 by the proposed score-based multi-omics in terms of the C-index (0.79 ± 0.03). CONCLUSIONS: Compared to single-source models, multi-omics integration has the potential to improve prediction performance, increase model stability, and reduce bias for both treatment response and survival analysis.

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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.

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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.

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BACKGROUND: Differences in responding to sensory stimuli, including sensory hyperreactivity (HYPER), hyporeactivity (HYPO), and sensory seeking (SEEK) have been observed in autistic individuals across sensory modalities, but few studies have examined the structure of these "supra-modal" traits in the autistic population. METHODS: Leveraging a combined sample of 3868 autistic youth drawn from 12 distinct data sources (ages 3-18 years and representing the full range of cognitive ability), the current study used modern psychometric and meta-analytic techniques to interrogate the latent structure and correlates of caregiver-reported HYPER, HYPO, and SEEK within and across sensory modalities. Bifactor statistical indices were used to both evaluate the strength of a "general response pattern" factor for each supra-modal construct and determine the added value of "modality-specific response pattern" scores (e.g., Visual HYPER). Bayesian random-effects integrative data analysis models were used to examine the clinical and demographic correlates of all interpretable HYPER, HYPO, and SEEK (sub)constructs. RESULTS: All modality-specific HYPER subconstructs could be reliably and validly measured, whereas certain modality-specific HYPO and SEEK subconstructs were psychometrically inadequate when measured using existing items. Bifactor analyses supported the validity of a supra-modal HYPER construct (ωH = .800) but not a supra-modal HYPO construct (ωH = .653), and supra-modal SEEK models suggested a more limited version of the construct that excluded some sensory modalities (ωH = .800; 4/7 modalities). Modality-specific subscales demonstrated significant added value for all response patterns. Meta-analytic correlations varied by construct, although sensory features tended to correlate most with other domains of core autism features and co-occurring psychiatric symptoms (with general HYPER and speech HYPO demonstrating the largest numbers of practically significant correlations). LIMITATIONS: Conclusions may not be generalizable beyond the specific pool of items used in the current study, which was limited to caregiver report of observable behaviors and excluded multisensory items that reflect many "real-world" sensory experiences. CONCLUSION: Of the three sensory response patterns, only HYPER demonstrated sufficient evidence for valid interpretation at the supra-modal level, whereas supra-modal HYPO/SEEK constructs demonstrated substantial psychometric limitations. For clinicians and researchers seeking to characterize sensory reactivity in autism, modality-specific response pattern scores may represent viable alternatives that overcome many of these limitations.

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Psychotic-like experiences (PLEs) are common throughout childhood, and the presence of these experiences is a significant risk factor for poor mental health later in development. Given the association of PLEs with a broad number of mental health diagnoses, these experiences serve as an important malleable target for early preventive interventions. However, little is known about these experiences across childhood. While these experiences may be common, longitudinal measurement in non-clinical settings is not. Therefore, in order to explore longitudinal trajectories of PLEs in childhood, we harmonized three school-based randomized control trials with longitudinal follow-up to identify heterogeneity in trajectories of these experiences. In an integrative data analysis (IDA) using growth mixture modeling, we identified three latent trajectory classes. One trajectory class was characterized by persistent PLEs, one was characterized by high initial probabilities but improving across the analytic period, and one was characterized by no reports of PLEs. Compared to the class without PLEs, those in the improving class were more likely to be male and have higher levels of aggressive and disruptive behavior at baseline. In addition to the substantive impact this work has on PLE research, we also discuss the methodological innovation as it relates to IDA. This IDA demonstrates the complexity of pooling data across multiple studies to estimate longitudinal mixture models.

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Life course epidemiology seeks to understand the intricate relationships between risk factors and health outcomes across different stages of life to inform prevention and intervention strategies to optimize health throughout the lifespan. However, extant evidence has predominantly been based on separate analyses of data from individual birth cohorts or panel studies, which may not be sufficient to unravel the complex interplay of risk and health across different contexts. We highlight the importance of a multi-study perspective that enables researchers to: (a) Compare and contrast findings from different contexts and populations, which can help identify generalizable patterns and context-specific factors; (b) Examine the robustness of associations and the potential for effect modification by factors such as age, sex, and socioeconomic status; and (c) Improve statistical power and precision by pooling data from multiple studies, thereby allowing for the investigation of rare exposures and outcomes. This integrative framework combines the advantages of multi-study data with a life course perspective to guide research in understanding life course risk and resilience on adult health outcomes by: (a) Encouraging the use of harmonized measures across studies to facilitate comparisons and synthesis of findings; (b) Promoting the adoption of advanced analytical techniques that can accommodate the complexities of multi-study, longitudinal data; and (c) Fostering collaboration between researchers, data repositories, and funding agencies to support the integration of longitudinal data from diverse sources. An integrative approach can help inform the development of individualized risk scores and personalized interventions to promote health and well-being at various life stages.

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Manhood is a precarious state that men seek to prove through the performance of masculine behaviors-including, at times, acts of aggression. Although correlational work has demonstrated a link between chronic masculine insecurity and political aggression (i.e., support for policies and candidates that communicate toughness and strength), experimental work on the topic is sparse. Existing studies also provide little insight into which men-liberal or conservative-are most likely to display increased political aggression after threats to their masculinity. The present work thus examines the effects of masculinity threat on liberal and conservative men's tendency toward political aggression. We exposed liberal and conservative men to various masculinity threats, providing them with feminine feedback about their personality traits (Experiment 1), having them paint their nails (Experiment 2), and leading them to believe that they were physically weak (Experiment 3). Across experiments, and contrary to our initial expectations, threat increased liberal-but not conservative-men's preference for a wide range of aggressive political policies and behaviors (e.g., the death penalty, bombing an enemy country). Integrative data analysis (IDA) reveals significant heterogeneity in the influence of different threats on liberal men's political aggression, the most effective of which was intimations of physical weakness. A multiverse analysis suggests that these findings are robust across a range of reasonable data-treatment and modeling choices. Possible sources of liberal men's heightened responsiveness to manhood threats are discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s11199-023-01349-x.

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Without preventative intervention, youth with a history of foster care (FC) involvement have a high likelihood of developing depression and anxiety (DA) symptoms. The current study used integrative data analysis to harmonize data across four foster and kinship parent-mediated interventions (and seven randomized control trials) designed to reduce youth externalizing and other problem behaviors to determine if, and for how long, these interventions may have crossover effects on youth DA symptoms. Moderation of intervention effects by youth biological sex, developmental period, number of prior placements, and race/ethnicity was also examined. Youth (N = 1891; 59% female; ages 4 to 18 years) behaviors were assessed via the Child Behavior Checklist, Parent Daily Report, and Eyberg Child Behavior Inventory at baseline, the end of the interventions (4-6 months post baseline), and two follow-up assessments (9-12 months and 18-24 months post baseline), yielding 4830 total youth-by-time assessments. The interventions were effective at reducing DA symptoms at the end of the interventions; however, effects were only sustained for one program at the follow-up assessments. No moderation effects were found. The current study indicates that parent-mediated interventions implemented during childhood or adolescence aimed at reducing externalizing and other problem behaviors had crossover effects on youth DA symptoms at the end of the interventions. Such intervention effects were sustained 12 and 24 months later only for the most at-risk youth involved in the most intensive intervention.

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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.

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Integrative data analysis (IDA) is an analytic tool that allows researchers to combine raw data across multiple, independent studies, providing improved measurement of latent constructs as compared to single study analysis or meta-analyses. This is often achieved through implementation of moderated nonlinear factor analysis (MNLFA), an advanced modeling approach that allows for covariate moderation of item and factor parameters. The current paper provides an overview of this modeling technique, highlighting distinct advantages most apt for IDA. We further illustrate the complex modeling building process involved in MNLFA by providing a tutorial using empirical data from five separate prevention trials. The code and data used for analyses are also provided.

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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.

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Understanding dementia and cognitive impairment is a global effort needing data from multiple sources across diverse ethno-regional groups. Methodological heterogeneity means that these data often require harmonization to make them comparable before analysis. We discuss the benefits and challenges of harmonization, both retrospective and prospective, broadly and with a focus on data types that require particular sorts of approaches, including neuropsychological test scores and neuroimaging data. Throughout our discussion, we illustrate general principles and give examples of specific approaches in the context of contemporary research in dementia and cognitive impairment from around the world.

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The transition from Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR) to Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5) attention deficit/hyperactivity disorder (ADHD) checklists included item wording changes that require psychometric validation. A large sample of 854 adolescents across four randomized trials of psychosocial ADHD treatments was used to evaluate the comparability of the DSM-IV-TR and DSM-5 versions of the ADHD symptom checklist. Item response theory (IRT) was used to evaluate item characteristics and determine differences across versions and studies. Item characteristics varied across items. No consistent differences in item characteristics were found across versions. Some differences emerged between studies. IRT models were used to create continuous, harmonized scores that take item, study, and version differences into account and are therefore comparable. DSM-IV-TR ADHD checklists will generalize to the DSM-5 era. Researchers should consider using modern measurement methods (such as IRT) to better understand items and create continuous variables that better reflect the variability in their samples.

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Psychiatric epidemiologists, developmental psychopathologists, prevention scientists, and treatment researchers have long speculated that treating child anxiety disorders could prevent alcohol and other drug use disorders in young adulthood. A primary challenge in examining long-term effects of anxiety disorder treatment from randomized controlled trials is that all participants receive an immediate or delayed study-related treatment prior to long-term follow-up assessment. Thus, if a long-term follow-up is conducted, a comparison condition no longer exists within the trial. Quasi-experimental designs (QEDs) pairing such clinical samples with comparable untreated epidemiological samples offer a method of addressing this challenge. Selection bias, often a concern in QEDs, can be mitigated by propensity score weighting. A second challenge may arise because the clinical and epidemiological studies may not have used identical measures, necessitating Integrative Data Analysis (IDA) for measure harmonization and scale score estimation. The present study uses a combination of propensity score weighting, zero-inflated mixture moderated nonlinear factor analysis (ZIM-MNLFA), and potential outcomes mediation in a child anxiety treatment QED/IDA (n = 396). Under propensity score-weighted potential outcomes mediation, CBT led to reductions in substance use disorder severity, the effects of which were mediated by reductions in anxiety severity in young adulthood. Sensitivity analyses highlighted the importance of attending to multiple types of bias. This study illustrates how hybrid QED/IDAs can be used in secondary prevention contexts for improved measurement and causal inference, particularly when control participants in clinical trials receive study-related treatment prior to long-term assessment.

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Recent research has suggested the importance of understanding for whom programs are most effective (Supplee et al., 2013) and that multidimensional profiles of risk and protective factors may moderate the effectiveness of programs (Lanza & Rhoades, 2012). For school-based prevention programs, moderators of program effectiveness may occur at both the individual and school levels. However, due to the relatively small number of schools in most individual trials, integrative data analysis across multiple studies may be necessary to fully understand the multidimensional individual and school factors that may influence program effectiveness. In this study, we applied multilevel latent class analysis to integrated data across four studies of a middle school pregnancy prevention program to examine moderators of program effectiveness on initiation of vaginal sex. Findings suggest that the program may be particularly effective for schools with USA-born students who speak another language at home. In addition, findings suggest potential positive outcomes of the program for individuals who are lower risk and engaging in normative dating or individuals with family risk. Findings suggest potential mechanisms by which teen pregnancy prevention programs may be effective.

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Despite decades of concern about disparities in educational outcomes for low SES students and students of color, there has been limited rigorous study of programmatic approaches for reducing these disparities in elementary or middle schools. We conducted integrative data analysis (IDA) of the combined data from eight Institute of Education Sciences funded cluster randomized trials to address the research gaps on social and behavioral outcome disparities. The final analytic sample includes 90,880 students in varying grade levels from kindergarten to grade 8 in 387 schools in 4 states (Maryland, Missouri, Virginia, and Texas). Two-level hierarchical linear modeling was used for multilevel moderation analysis. This study provided empirical evidence that there were significant gender, racial, and socioeconomic disparities on social and behavioral outcome measures for elementary and middle school students, the disparities significantly varied across schools, and the disparities could be reduced by interventions. We discussed our findings, implications for interpreting effect sizes of interventions using disparities as empirical benchmarks, and study limitations. We concluded with suggestions for future research.

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