Daniel J. Bauer - US grants

The proposed project is designed to clarify, expand upon, and apply growth mixture modeling as it relates to understanding developmental pathways that lead to adolescent drug abuse and dependence. Growth mixture modeling techniques are recently emerging quantitative methods that permit investigators to identify discrete developmental patterns of change over time. These new methods hold great promise for the social sciences, arid especially for the study of pathways of drug use, because they enable investigators to construct taxonomies of normal and maladaptive developmental patterns. Each developmental pathway of risk may have a unique etiology or special portent for later psychopathology. Because little is known about these emergent methods, the primary goal of this proposal is to review and evaluate growth mixture models for their relevance to research in developmental psychopathology and drug abuse. The project is organized around three specific aims. Aim 1 is to review and contrast two recently developed growth mixture modeling techniques relative to one another and to more traditional analytic models. Aim 2 is to examine the behavior of growth mixture models with data simulated to reflect variations and conditions that would commonly be encountered in applied research on drug use and abuse. The results from Aims 1 and 2 will be applied in Aim 3, which provides a pedagogical demonstration of growth mixture modeling with real world data by testing the specific hypothesis that particular developmental pathways of antisocial behavior will predict adolescent drug abuse.

In the behavioral and social sciences, many key theoretical constructs, such as depression, product appeal, and social capital, cannot be measured directly. Hypotheses involving these latent constructs can nevertheless be evaluated by combining information from multiple imperfect and indirect measures (e.g., sadness, anergy, and self-derogation measurements of depression). Currently, the dominant methodology for testing hypotheses concerning latent variables is structural equation modeling (SEM). SEM shares the goal of factor analysis to summarize dependencies among observed variables in terms of an underlying set of latent variables, but it also permits the testing of functional relationships between latent variables. A key limitation of SEM, however, is that these relationships must be linear. This presents two key difficulties for research in the behavioral and social sciences. The first problem is how to determine whether this assumption is tenable. Because the variables of interest are latent and therefore lack observed, realized values, it is not possible to implement the same diagnostics routinely used with regression models for observed variables. This project investigates two potential solutions to this problem, the first involving the use of individual estimates for the latent variables and their residuals, and the second using a weighted locally linear approximation to produce a smoothed estimate of the underlying (possibly nonlinear) latent regression function. A second key problem is how to model nonlinear effects should they be detected. This project evaluates both parametric estimators and a new semi-parametric estimator for recovering nonlinear relationships between latent variables, considering the trade-off between bias and efficiency that occurs when models are imperfectly specified.

The behavioral and social sciences continue to struggle with adequate measurement and modeling techniques, given that many of the constructs of interest are not directly observable (i.e., latent). This project will improve the evaluation and specification of models involving latent variables. First, it will enable scientists to check the accuracy of their assumptions about how latent variables are related. Second, it will provide scientists with sound methods for evaluating nonlinear relationships among latent variables, avoiding the often unrealistic default assumption that these relationships follow straight lines. By thus enabling scientists to obtain a richer, more accurate understanding of how behavioral and social processes are related, the current project has the potential to improve research in many fields, including psychology, sociology, marketing, political science, health, and education.

DESCRIPTION (provided by applicant): Perennial challenges in the field of substance use include obtaining sufficient sample sizes to test low-base rate behaviors, comparing findings across a wide array of substance use measures and diagnostic instruments which may even rely on different raters, and evaluating the generalizability of findings across the highly heterogeneous population of substance users (e.g., subgroup comparisons). One methodological approach to addressing these challenges is Integrative Data Analysis (IDA) or the simultaneous analysis of data pooled from multiple studies. Recent research using IDA demonstrates the feasibility of this approach for studying substance use and disorder. The promise of IDA, however, rests on the availability of effective techniques for data harmonization, that is, the creation of substance use and substance use disorder measures that are equivalent in scale and meaning across studies despite variation in the primary measures originally used to assess the participants. The overarching goal of this proposal is thus to develop, refine, and evaluate measurement models that will expand the domain of potential measurement contexts to which harmonization techniques can be applied. Measurement contexts include variation in instrumentation, such as scales that differ in item overlap, content, and response format or options across studies, and variation in assessment source, such as self- versus peer-report. This goal is pursued through four aims: (1) to extend and evaluate psychometric models (e.g., item factor analysis) for harmonizing continuous outcomes including symptom severity and quantity/frequency of use, (2) to develop and evaluate psychometric models (e.g., latent class and mixture models) for harmonizing categorical outcomes including substance use diagnoses based on different instruments or versions of the DSM, and to extend these models to harmonize data obtained from multiple sources (self and peers) across studies for both (3) continuous and (4) categorical substance use outcomes. We will pursue these aims through a novel combination of computer simulation studies (to evaluate the statistical properties of harmonized scores obtained from data with known population parameters) and laboratory analogue studies (to evaluate the validity of harmonized scores in controlled conditions permitting unknown participant factors to influence responding across item set or study), yielding unique information about the conditions under which these psychometric models produce valid harmonized scores in practice. This research will provide both novel methods permitting the broader use of IDA in substance use research as well as new guidelines regarding the limits of IDA. Resulting public health benefits include advancing domains of substance use research that especially benefit from large sample sizes to increase power (e.g., GWAS) or observance of low-base rate behaviors (e.g., injection drug use and HIV-related behaviors), direct tests of replicability of novel hypotheses across study (e.g., GxE interactions in BG studies), or increased population diversity to examine the generalizability of effects across subgroups.

? DESCRIPTION (provided by applicant): The Center for Developmental Science (CDS) requests continued support for five predoctoral (two-year program) and five postdoctoral (two-year program) positions associated with its vibrant and accomplished training program, the Carolina Consortium on Human Development (CCHD; T32-HD007376). Located in the rich intellectual environment of central North Carolina, the program brings together a world-class faculty who come from four major research universities (UNC-Chapel Hill, Duke University, North Carolina State University, and UNC-Greensboro) and who span psychology, neuroscience, public health, nursing, education, psychiatry, sociology, public policy, and methodology. The 47 faculty mentors include leaders in the study of children's and adolescent's health and well-being with a strong record of research productivity, grant funding, and training. The program is based on the premise that training in Developmental Science provides a vitally important transdisciplinary model and an associated language for understanding a broad array of health outcomes (e.g., health-risk behaviors, obesity, self-regulation, resilience to early trauma and stress, cognitive functioning). Core principles of Developmental Science now permeate all major perspectives on health and well-being. These principles include, for example, the study of developmental processes (a) as occurring through multilevel, interacting causal fields ranging from culture to biology; (b) as embedded in temporal patterns across levels of analysis as reflected in the study of transitions, trajectories and plasticity; and (c) as incluing on-going bidirectional influences across levels of analysis. The CCHD program is distinctive in its focus on the articulation of these principles and their operationalization in empirical health research. The resulting structured-yet-flexible program is uniquely designed to provide training in core competency areas as well as individually tailored domains. In addition to common elements (i.e., the CCHD proseminar series, research apprenticeships with faculty mentors, and professional and research skill development workshops), trainees select from an extensive menu of tailored experiences that are specific to their training goals as identified through an Individualized Development Plan. We continue to monitor and refine our training program through an extensive evaluation process that involves trainees, mentors, and a national Advisory Board. A total of 54 predoctoral and 29 postdoctoral trainees participated in the program during the last reporting period. The trainees have obtained excellent academic and research positions, have published actively in the research literature, and have shown early success in obtaining grant funding. This track record confirms the effectiveness of the program. The over-arching goal of the CCHD is to give a foundation in Developmental Science to the next generation of scholars as they prepare for innovative and productive research careers. Our trainees speak the language of sophisticated transdisciplinary teams that have the power to transform the scientific study of the origins, natural history, and consequences of health.