Integrative Data Analysis Approach Research Articles

Legislative Power over Mineral Resources in the Ethiopian Federation: Legal and Practical Challenges

The assignment of legislative power over mineral resources is a highly contentious issue in federal constitutional design. This article aims to shed light on this issue by examining the assignment and exercise of legislative powers over mineral resources in the Ethiopian Federation. A qualitative research approach was used which included analysis of laws, examining documents and in-depth interviews. The legal framework, policy documents, and decisions of government officials relating to legislative authority over mineral resources were scrutinized. Furthermore, interviews were conducted with key stakeholders involved in the matter to gain an understanding of the actual challenges and difficulties associated with the exercise of this power. An integrated data analysis approach is used. According to the analysis, the Constitution of the Federal Democratic Republic of Ethiopia lacks clarity regarding legislative power over mineral resources. The study also reveals significant gaps in the institutional and legal frameworks governing the exercise of legislative power over mineral resources. These gaps have also resulted in the development of a legal framework that contradicts the principles laid out in the Constitution. The findings highlight the need to address the legal and practical challenges within the Ethiopian Federation.

Social Health and Change in Cognitive Capability among Older Adults: Findings from Four European Longitudinal Studies

Introduction: In this study, we examine whether social health markers measured at baseline are associated with differences in cognitive capability and the rate of cognitive decline over an 11-to-18-year period among older adults and compare results across studies. Methods: We applied an integrated data analysis approach to 16,858 participants (mean age 65 years; 56% female) from the National Survey for Health and Development (NSHD), the English Longitudinal Study of Aging (ELSA), the Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), and the Rotterdam Study. We used multilevel models to examine social health in relation to cognitive capability and the rate of cognitive decline. Results: Pooled estimates show distinct relationships between markers of social health and cognitive domains, e.g., a large network size (≥6 people vs. none) was associated with higher executive function (0.17 standard deviation [SD] [95% CI: 0.00, 0.34], I2 = 27%) but not with memory (0.08 SD [95% CI: −0.02, 0.18], I2 = 19%). We also observed pooled associations between being married or cohabiting, having a large network size, and participating in social activities with slower decline in cognitive capability. However, estimates were close to zero, e.g., 0.01 SD/year (95% CI: 0.01, 0.02) I2 = 19% for marital status and executive function. There were clear study-specific differences: results for average processing speed were the most homogenous, and results for average memory were the most heterogeneous. Conclusion: Overall, markers of good social health have a positive association with cognitive capability. However, we found differential associations between specific markers of social health and cognitive domains and differences between studies. These findings highlight the importance of examining between-study differences and considering the context specificity of findings in developing and deploying interventions.

Have genetic targets for faecal pollution diagnostics and source tracking revolutionised water quality analysis yet?

The impacts on faecal pollution analysis using nucleic acid-based methods, such as PCR and sequencing, in health-related water quality research were assessed by rigorous literature analysis. A wide range of application areas and study designs has been identified since the first application more than 30 years ago (>1,100 publications). Given the consistency of methods and assessment types, we suggest defining this emerging part of science as a new discipline: genetic faecal pollution diagnostics (GFPD) in health-related microbial water quality analysis. Undoubtedly, GFPD has already revolutionised faecal pollution detection and microbial source tracking, the current core applications. GFPD is also expanding to many other research areas, including infection and health risk assessment, evaluation of microbial water treatment, and support of wastewater surveillance. In addition, storage of DNA extracts allows for biobanking, which opens up new perspectives. The tools of GFPD can be combined with cultivation-based standardised faecal indicator enumeration, pathogen detection, and various environmental data types, in an integrated data analysis approach. This comprehensive meta-analysis provides the scientific status quo of this field, including trend analyses and literature statistics, outlining identified application areas, and discussing the benefits and challenges of nucleic acid-based analysis in GFPD.

Investigating genes associated with cardiovascular disease among heart failure patients for translational research and precision medicine

AbstractBackgroundCardiovascular disease (CVD) is a leading cause of premature mortality in the United States and the world. CVD comprises several complex and mostly heritable conditions, which range from myocardial infarction to congenital heart disease. The risk factors contributing to the development of CVD and response to therapy in an individual patient are highly variable. Here, we report our findings from an integrative analysis of gene expression, disease‐causing gene variants and associated phenotypes among CVD populations, with a focus on high‐risk heart failure (HF) patients.MethodsWe built a cohort using electronic health records of consented patients with available samples and then performed high‐throughput whole genome and RNA sequencing of key genes responsible for HF and other CVD pathologies. Our in‐depth gene expression analysis revealed differentially expressed genes associated with HF and other CVDs. We performed a variant analysis of whole genome sequence data of CVD patients and identified genes with altered gene expression with functional and non‐functional mutations in these genes.ResultsOur results highlight the importance of investigating the mechanisms of CVD progression through multi‐omics datasets. Next, we performed splice mutation and variant distribution analysis of genes associated with HF and other CVD. We implemented Jensen–Shannon divergence (JSD)‐based method and identified HBA1, FADD, ADRB2, NPPB, ADRB1, ADB and NPPC genes with the greatest variance based on their JSD scores. Our study provided evidence that applying integrative data analysis approach involving genomics and transcriptomics data will not only help understand the pathophysiology of CVD diseases but also reduce heterogeneity in disease subtypes.

Satisfying singlehood as a function of age and cohort: Satisfaction with being single increases with age after midlife.

A growing body of research suggests that despite the stereotype of being dissatisfied with their relationship status, there is variability in how single (unpartnered) individuals feel about singlehood. The current research examined how satisfaction with singlehood varies (linearly or nonlinearly) with age. In Study 1, we analyzed five cross-sectional samples of single individuals (N = 3,304; collected in 2020-2021) using an integrative data analysis (IDA) approach. In Study 2, we used Dutch longitudinal data (N = 3,193; collected in 2008-2019) to more precisely separate the effect of age from that of birth cohort. Study 1 demonstrated that satisfaction with singlehood was positively associated with age after midlife whereas desire for a partner was negatively associated with age. Study 2 provided conceptually consistent evidence for age-related increases in satisfaction with singlehood during mid to late adulthood (around 40s-80s). Some evidence was found in Study 2 that more recent cohorts were higher in satisfaction with singlehood, but this effect did not hold when accounting for differences in marital status. These results provide evidence for potential age effects in well-being related outcomes for singles and suggest that midlife may be an important turning point. Understanding what makes singles satisfied with singlehood at older age may be a promising approach to gain insights into how to promote well-being of the rising single population. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Data-Analysis Approach Aids Reservoir-Quality Prediction of Microporous Carbonates

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 19972, “Improving Reservoir-Quality Prediction of Microporous Carbonates Using a Multiscale Geophysical Data-Analysis Approach,” by Ahmad Ramdani, SPE, Viswasanthi Chandra, and Thomas Finkbeiner, SPE, King Abdullah University of Science and Technology. The paper has not been peer reviewed. Copyright 2021 International Petroleum Technology Conference. Reproduced by permission. Understanding the effect of microporosity on reservoir quality and predicting the spatial distribution of microporosity at the reservoir scale accurately is required to improve the recovery of remaining hydrocarbons from reservoirs. Using an integrated data-analysis approach applied to multiscale geological and geophysical data sets from micrometer-scale scanning-electron-microscope (SEM) imagery to decameter-scale seismic data, the authors predict the distribution of microporosity at the reservoir gridblock scale. The proposed methodology is applied in the complete paper to the Arab-D reservoir equivalent outcrop data from the Upper Jubaila formation in Saudi Arabia. Methodology This methodology is tested at an 800-m-long roadcut along the Riyadh-Mecca Highway in Saudi Arabia, stratigraphically equivalent to the Upper Jubaila Formation in the subsurface (Fig. 1). A 35-m-long core was drilled from the top of the same roadcut (dubbed the DQ-1 well), from which 106 cylindrical horizontal core plugs were obtained, drilled with an average spacing of 30 cm. This outcrop location was chosen because the pore volume in this interval is characterized by porosity with pore-diameter distribution of less than 10 µm. This outcrop provides an excellent analog to study microporosity-dominated carbonate rocks irrespective of their diagenetic origin. Moreover, the Upper Jubaila formation is a part of the Arab-D reservoir in the subsurface, which has been acclaimed as the most prolific oil reservoir in the world. Because this study used outcropped rocks, the authors acknowledge that differences in petrophysical properties with the subsurface rock formations will exist. Therefore, the study is not intended as a one-to-one analog for the Arab-D reservoir. Instead, the aim is to establish a general methodology that links micrometer-scale morphology of microcrystals that hosts microporosity with macroscale petrophysical properties and seismic attributes. Details of the methodology of seismic data acquisition and petrographic and petrophysical analysis are provided in the complete paper.

Abstract IA-21: Applying a data integrative and convergence epidemiology approach to study multilevel risk factors for cancer in distinct AANHPI populations

Abstract Background: For Asian American, Native Hawaiian and Pacific Islander (AANHPI) females, lung cancer is one of the most common cancers and the leading cause of cancer death. More than half of lung cancers among AANHPI females occur among never-smokers, but incidence rates of lung cancer according to smoking status have not been available. Purpose: With a large, integrated dataset of electronic health record data from two healthcare systems—Sutter Health in Northern California and Kaiser Permanente Hawai'i—linked to state cancer registry data on incident lung cancer diagnoses 2000-2013, we describe incidence of lung cancer according to smoking status among females across detailed race and ethnicity. Methods: We calculated age-adjusted incidence rates for lung cancer according to smoking status and detailed race and ethnicity among females, focusing on AANHPI ethnic groups, and assessed relative incidence across racial and ethnic groups. The study population included N=1,222,694 females (n=244,147 AANHPI), n=3,297 (n=535) of whom were diagnosed with lung cancer. We examined relative incidence across group defined by detailed race and ethnicity. We also provided incidence of lung cancer among AANHPI males who never smoked in a supplement. Results: Among AANHPI female groups, proportions of lung cancers among never-smokers ranged from 25% among Native Hawaiian to 80% among Chinese females. Incidence of lung cancer among never-smoking AANHPI females as an aggregate was 17.1 per 100,000 (95% CI: 14.9, 19.4), but rates varied widely across ethnic groups. Never-smoking Chinese females had the highest rate (22.8; 95% CI: 17.3, 29.1). Except for Japanese females, incidence among every never-smoking AANHPI female ethnic group was higher than that of all never-smoking females combined. Never-smoking AANHPI males also have higher incidence of lung cancer compared to other groups defined by race and ethnicity. Conclusions: The integrative data analysis approach offers great advantages over traditional cancer cohorts, but it does require substantial time and effort to assure data confidentiality, integrity, and transparency to provide robust results. However, with convergence epidemiology—in this case leveraging needed expertise in data science and analysis to answer an epidemiology question—it is also a valuable approach to study disparate cancer outcomes among small populations. Illustrating this, our study is the first to document high rates of lung cancer among never-smoking AANHPI ethnic groups, dispels the myth that AANHPI females are at overall reduced risk of lung cancer, and demonstrates the need to disaggregate this highly diverse population. Results should inform lung cancer prevention strategies among AANHPI populations. Citation Format: MIndy C. DeRouen, Alison J. Canchola, Caroline A. Thompson, Anqi Jin, Sixiang Nie, Carmen Wong, Daphne Lichtensztajn, Laura Allen, Manali I. Patel, Yihe G. Daida, Harold S. Luft, Salma Shariff-Marco, Peggy Reynolds, Heather A. Wakelee, Su-Ying Liang, Beth E. Waitzfelder, Iona Cheng, Scarlett L. Gomez. Applying a data integrative and convergence epidemiology approach to study multilevel risk factors for cancer in distinct AANHPI populations [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr IA-21.

Determinants of Fruit Purchasing Decision Among Singaporean Consumers: An Empirical Study

Empirical assessment concerning the relationship between fruit characteristics and fruit purchase is rather limited within the Asia-Pacific region. It is for this reason that the current study was carried out, to offer an understanding on the fruits characteristics that consumers seek during their fruit purchasing decision. This study was undertaken among 519 Singaporeans. The integrative data analysis approach, i.e., PCA and PLS-SEM adopted in this study is among the first to be undertaken in a consumer behavior study on perishables. The three determinants influencing fruit purchase decision include search and experience, claims and label, and visual appearance. Claims and label, and visual appearance, respectively, demonstrated positive relationship with fruit purchase, while search and experience depicted an inverse relationship. This study findings offer valuable insights which can assist food supply stakeholders to strategize and adopt approaches that serve as a move to tackle low fruit intake among Singaporeans.

Predicting positive outcomes and successful completion in an online parenting program for parents of children with disruptive behavior: An integrated data analysis

Online delivery of parenting support is steadily increasing, yet the factors that influence program engagement and efficacy are still understudied. This study used an integrated data analysis approach to identify family and program-related factors that influence outcomes. We combined individual data from seven published efficacy trials of the web-based version of the Triple P—Positive Parenting Program. Data were analyzed for 985 families with children aged between 2 and 12 years (M = 4.87; SD = 2.14) using a Latent Change Score approach. At post-intervention, sociodemographic factors were not predictive of changes in child behavior problems, while parents of boys and those with higher education showed greater improvements in dysfunctional parenting. Parents who were initially more confident in their parenting showed more overall gains while parents with more initial adjustment difficulties showed less improvement. Only the effect of baseline child behavior problems on changes in dysfunctional parenting was moderated by treatment condition. At follow-up, program variant and completion were the primary outcome predictors, with completion found to be related to initial parenting confidence, internet usage and program variant. The implications of these findings for reaching and retaining parents in online programs across all phases of the engagement process are discussed.

Estimation and Uncertainties of Profiles and Equilibria for Fusion Modeling Codes

The results of transport modeling codes, e.g., GENE for the plasma core or SOLPS-ITER for the plasma edge, depend critically on reliable profile and equilibrium estimates. The propagation of uncertainties (UP) of input quantities to the results of modeling codes, e.g., power and particle exhaust and plasma stability, is frequently neglected because of the costs of running the codes as well as because of the missing uncertainty quantification of input quantities. The situation becomes even more cumbersome if profile gradients and their uncertainties are of major concern for transport analyses. Two different techniques are presented to estimate profiles, profile gradients, their uncertainties, and candidate profiles for UP in modeling codes. Markov Chain Monte Carlo sampling of the posterior probability density of an integrated data analysis approach is applied to estimate electron density and temperature profiles. Nonstationary Gaussian process regression is applied to estimate ion temperature and angular velocity profiles. Both methods provide in a natural way profile gradients, profile logarithmic gradients, and their uncertainties. Modeling codes benefit also from reliable equilibrium reconstructions and quantification of the uncertainty of various equilibrium parameters. For the analysis of diagnostics data, the position and uncertainty of flux surfaces as well as of the magnetic axis are important. For plasma transport and stability codes, the estimation of uncertainties of current and q-profiles is presented. For plasma edge codes the position of the separatrix contour and its uncertainty at various poloidal positions is of primary interest especially if steep profile gradients are present. Examples of uncertainties and their sources in magnetic scalar quantities, profiles, and separatrix contours are shown.

Heterogeneity in Association Between Cognitive Function and Gait Speed Among Older Adults: An Integrative Data Analysis Study.

Increasing evidence shows that cognition and gait speed are associated and are important measures of health among older adults. However, previous studies have used different methods to assess these 2 outcomes and lack sufficient sample size to examine heterogeneity among subgroups. This study examined how the relationship between global cognitive function and gait speed are influenced by age, gender, and race utilizing an integrated data analysis approach. Data on cognition (Montreal Cognitive Assessment [MoCA], Mini-Mental Status Examination [MMSE], and Modified Mini-Mental State Examination [3MSE]) and gait speed (range: 4-400 m) were acquired and harmonized from 25 research studies (n = 2802) of adults aged 50+ from the Wake Forest Older American Independence Center. Multilevel regression models examined the relationship between predicted values of global cognitive function (MoCA) and gait speed (4-m walk), including heterogeneity by age, race, and gender. Global cognitive function and gait speed exhibited a consistent positive relationship among whites with increasing age, while this was less consistent for African Americans. That is, there was a low correlation between global cognitive function and gait speed among African Americans aged 50-59, a positive correlation in their 60s and 70s, then a negative correlation thereafter. Global cognition and gait speed exhibited a curvilinear U-shaped relationship among whites; however, the association becomes inverse in African Americans. More research is needed to understand this racial divergence and could aid in identifying interventions to maintain cognitive and gait abilities across subgroups.

The ADHD teen integrative data analysis longitudinal (TIDAL) dataset: background, methodology, and aims

BackgroundThe Attention Deficit Hyperactivity Disorder (ADHD) Teen Integrative Data Analysis Longitudinal (TIDAL) dataset integrates data from four randomized trials.MethodParticipants with ADHD (N = 854; 72.5% male, 92.5% racial/ethnic minority, ages 10–17) were assessed three times across 12 months. Data includes parent, self, and teacher ratings, observations, and school records. The battery was harmonized using an Integrative Data Analysis (IDA) approach to form variables that assign unique values to all participants.ResultsThe data will be used to investigate: (1) profiles that organize the heterogeneous population into clinically meaningful subgroups, (2) whether these profiles predict treatment response, (3) heterogeneity in treatment response and variables that predict this response, (4) how treatment characteristics and adjunctive supports predict treatment response, and (5) mediators of treatment and whether these mechanisms are moderated by treatment characteristics.ConclusionsThe ADHD TIDAL Dataset will be openly shared with the field to maximize its utility.

Exploiting market-oriented collective learning cycle to leverage competitive advantage at a foreign subsidiary in emerging markets

PurposeThe purpose of this paper is to examine how a foreign subsidiary operates in emerging markets and integrates market orientation with organizational learning to achieve a competitive lead. It is an attempt to fill an evident gap in the literature of integrating organizational learning into a market-oriented competitive strategy through using a four-step collective learning cycle at General Motors Egypt (GME).Design/methodology/approachThe paper adopts a qualitative case study methodology to thoroughly examine the viewpoints of 90 respondents via in-depth and unstructured interviews with both managers and employees working in a variety of divisions inside GME. An integrative qualitative data analysis approach is used to explore, synthesize, interpret and derive relationships resulting from the collected data.FindingsThis work advances the theory of organizational learning by testing the theme of collective learning cycle in a real work setting. It presents a real example of aligning market orientation into a collective learning cycle directed toward achieving competitive advantages.Research limitations/implicationsThis study provides scholars and practitioners alike with a real scenario on how and why a four-step organizational learning cycle functions as a building block to generate a competitive advantage. It also discusses the elements of collective learning that are not captured by the four-step collective learning cycle. Factors facilitating market-based organizational learning are also explored. However, the results generated are contingent on the investigated case study circumstances, which are limited in generalizability.Practical implicationsThe paper addresses a set of directions through which auto assembly firms leverage both collective learning practices and knowledge-driven strategy to gain competitive advantages. The GME paradigm indicates how a firm can use collective learning not only to respond to an internal need for change but also to react to external market forces and constraints.Originality/valueThis study is the first of its kind to investigate the value of the cyclic learning concept from a strategic viewpoint in a multinational organizational context. It enriches the primarily practitioner literature on aligning collective learning into strategy with rich empirical examination of the learning practices of a leading foreign subsidiary. It resolves a gap in the literature regarding how organizational learning and knowledge management processes are aligned to market-oriented competitive strategy. The paper draws a number of critical research issues that call for refinement of the organizational learning cycle theory.

Is Healthy Neuroticism Associated with Health Behaviors? A Coordinated Integrative Data Analysis.

Current literature suggests that neuroticism is positively associated with maladaptive life choices, likelihood of disease, and mortality. However, recent research has identified circumstances under which neuroticism is associated with positive outcomes. The current project examined whether "healthy neuroticism", defined as the interaction of neuroticism and conscientiousness, was associated with the following health behaviors: smoking, alcohol consumption, and physical activity. Using a pre-registered multi-study coordinated integrative data analysis (IDA) approach, we investigated whether "healthy neuroticism" predicted the odds of engaging in each of the aforementioned activities. Each study estimated identical models, using the same covariates and data transformations, enabling optimal comparability of results. These results were then meta-analyzed in order to estimate an average (N-weighted) effect and to ascertain the extent of heterogeneity in the effects. Overall, these results suggest that neuroticism alone was not related to health behaviors, while individuals higher in conscientiousness were less likely to be smokers or drinkers, and more likely to engage in physical activity. In terms of the healthy neuroticism interaction of neuroticism and conscientiousness, significant interactions for smoking and physical activity suggest that the association between neuroticism and health behaviors was smaller among those high in conscientiousness. These findings lend credence to the idea that healthy neuroticism may be linked to certain health behaviors and that these effects are generalizable across several heterogeneous samples.

Developmental Changes in Sexual Risk and Substance Use Among African American Females: an Integrated Data Analysis Approach Using Time-varying Effect Models.

The aim of this study was to describe age-related changes in sexual risk and substance use using existing data from three HIV/sexually transmitted infection (STI) prevention trials conducted in Atlanta, Georgia, that enrolled young African American women. We used two novel analysis methods: integrative data analysis (IDA) and time-varying effect models (TVEM). Each trial collected self-reported behavioral data and vaginal swab specimens assayed for chlamydia, gonorrhea, and trichomonas. Baseline data from all participants and follow-up data from participants not randomized to an active intervention arm were integrated in a pooled dataset using an IDA approach. The pooled dataset included observations for 1974 individuals, aged 14-25years for behavioral outcomes and 16-25years for STI outcomes. We used TVEM to model age-related changes in sexual risk and substance use behaviors and STI acquisition, adjusting for trial as a fixed effect. Coital frequency and condomless sex increased through the early 20s while multiple partnerships remained fairly steady. Alcohol use and cigarette smoking increased through about age 24years before declining. Marijuana use peaked at age 16-17years and thereafter generally declined. STI acquisition was highest at age 16years. This study demonstrates the feasibility and utility of innovative methodological techniques to address novel questions related to adolescent development using existing data from multiple trials. The results suggest that mid-adolescence and the early 20s may be periods of particular risk. The findings may be useful for timing culturally and developmentally relevant prevention interventions for young African American women.

AP001056.1, A Prognosis-Related Enhancer RNA in Squamous Cell Carcinoma of the Head and Neck.

A growing number of long non-coding RNAs (lncRNAs) have been linked to squamous cell carcinoma of the head and neck (SCCHN). A subclass of lncRNAs, termed enhancer RNAs (eRNAs), are derived from enhancer regions and could contribute to enhancer function. In this study, we developed an integrated data analysis approach to identify key eRNAs in SCCHN. Tissue-specific enhancer-derived RNAs and their regulated genes previously predicted using the computational pipeline PreSTIGE, were considered as putative eRNA-target pairs. The interactive web servers, TANRIC (the Atlas of Noncoding RNAs in Cancer) and cBioPortal, were used to explore the RNA levels and clinical data from the Cancer Genome Atlas (TCGA) project. Requiring that key eRNAs should show significant associations with overall survival (Kaplan–Meier log-rank test, p < 0.05) and the predicted target (correlation coefficient r > 0.4, p < 0.001), we identified five key eRNA candidates. The most significant survival-associated eRNA was AP001056.1 with ICOSLG encoding an immune checkpoint protein as its regulated target. Another 1640 genes also showed significant correlation with AP001056.1 (r > 0.4, p < 0.001), with the “immune system process” being the most significantly enriched biological process (adjusted p < 0.001). Our results suggest that AP001056.1 is a key immune-related eRNA in SCCHN with a positive impact on clinical outcome.

The Relation of Personality and Intelligence-What Can the Brunswik Symmetry Principle Tell Us?

Personality and intelligence are defined as hierarchical constructs, ranging from broad g-factors to (domain-)specific constructs. The present study investigated whether different combinations of hierarchical levels lead to different personality-intelligence correlations. Based on the integrative data analysis approach, we combined a total of five data sets. The focus of the first study (N = 682) was an elaborated measurement of personality (NEO-PI-R), which was applied with a relatively short intelligence test (Intelligence Structure Test 2000 R). In the second study (N = 413), a comprehensive measurement of intelligence (Berlin Intelligence Structure test) was used with a shorter personality questionnaire (NEO-FFI). In line with the Brunswik symmetry principle, the findings emphasize that personality-intelligence correlations varied greatly across the hierarchical levels of constructs considered in the analysis. On average, Openness showed the largest relation with intelligence. We recommend for future studies to investigate personality-intelligence relations at more fine-grained levels based on elaborated measurements of both personality and intelligence.

Impact of Behavioral Drug Abuse Treatment on Sexual Risk Behaviors: An Integrative Data Analysis of Eight Trials Conducted Within the National Drug Abuse Treatment Clinical Trials Network.

The extent to which behavioral drug abuse treatments affect sexual risk behaviors is largely unknown. This study examined the impact of behavioral drug abuse treatments on sexual risk behaviors using an integrative data analysis approach across eight trials conducted within the National Drug Abuse Treatment Clinical Trials Network (CTN). Participants (N = 1305) from eight randomized controlled trials who were sexually active at baseline were included in the pooled dataset; 48.7% were female, 64.1% self-identified as a racial/ethnic minority, with M (SD) age of 34.9 (9.6). Longitudinal logistic regression estimated the probability of risky sexual behavior (i.e., inconsistent condom use and/or > 1 sexual partner in past 30days) post-intervention with an indicator variable (1 for post-intervention), study condition (control, intervention), and their interaction as predictors; the analysis employed random effects for each trial and included relevant control variables. Time-varying differences in effects based on weeks post-intervention were incorporated using interacted linear and quadratic terms with condition status. Approximately 84.2% reported risky sexual behaviors at baseline. The control and intervention conditions were 18.5 and 17.3 percentage points less likely to report risky sexual behavior post-intervention, respectively. Results suggest decreasing rates of risky sex engagement until 8weeks (control) or 9weeks (intervention) post-intervention; risky sexual behavior subsequently increased. Behavioral CTN trial participation was associated with decreased sexual risk behaviors in both the intervention and control trial conditions. Participation in behavioral substance use treatment may result in secondary benefits of sexual risk behavior reductions.

Modeling Pathways of Character Development across the First Three Decades of Life: An Application of Integrative Data Analysis Techniques to Understanding the Development of Hopeful Future Expectations.

There were two purposes of the present research: first, to add to scholarship about a key character virtue, hopeful future expectations; and second, to demonstrate a recent innovation in longitudinal methodology that may be especially useful in enhancing the understanding of the developmental course of hopeful future expectations and other character virtues that have been the focus of recent scholarship in youth development. Burgeoning interest in character development has led to a proliferation of short-term, longitudinal studies on character. These data sets are sometimes limited in their ability to model character development trajectories due to low power or relatively brief time spans assessed. However, the integrative data analysis approach allows researchers to pool raw data across studies in order to fit one model to an aggregated data set. The purpose of this article is to demonstrate the promises and challenges of this new tool for modeling character development. We used data from four studies evaluating youth character strengths in different settings to fit latent growth curve models of hopeful future expectations from participants aged 7 through 26 years. We describe the analytic strategy for pooling the data and modeling the growth curves. Implications for future research are discussed in regard to the advantages of integrative data analysis. Finally, we discuss issues researchers should consider when applying these techniques in their own work.

Two-Year Impact of Prevention Programs on Adolescent Depression: an Integrative Data Analysis Approach.

This paper presents the first findings of an integrative data analysis of individual-level data from 19 adolescent depression prevention trials (n = 5210) involving nine distinct interventions across 2years post-randomization. In separate papers, several interventions have been found to decrease the risk of depressive disorders or elevated depressive/internalizing symptoms among youth. One type of intervention specifically targets youth without a depressive disorder who are at risk due to elevated depressive symptoms and/or having a parent with a depressive disorder. A second type of intervention targets two broad domains: prevention of problem behaviors, which we define as drug use/abuse, sexual risk behaviors, conduct disorder, or other externalizing problems, and general mental health. Most of these latter interventions improve parenting or family factors. We examined the shared and unique effects of these interventions by level of baseline youth depressive symptoms, sociodemographic characteristics of the youth (age, sex, parent education, and family income), type of intervention, and mode of intervention delivery to the youth, parent(s), or both. We harmonized eight different measures of depression utilized across these trials and used growth models to evaluate intervention impact over 2years. We found a significant overall effect of these interventions on reducing depressive symptoms over 2years and a stronger impact among those interventions that targeted depression specifically rather than problem behaviors or general mental health, especially when baseline symptoms were high. Implications for improving population-level impact are discussed.