Most studies examining predictors of treatment outcomes among problem drinkers have used a traditional statistical approach that examines group outcomes (e.g. analysis of variance, multiple regression analysis). Contrary to traditional methods, a person-centered approach identifies commonalities among clusters of individuals and provides the opportunity to examine the relationship between multiple individual differences and outcomes in a longitudinal manner. Specifically, the person-centered approach makes it possible to cluster individuals into subgroups based on their change patterns, and to examine the relationship between those subgroups and other variables of interest (e.g., drinking problem severity). This approach allows the inclusion of a relatively large number of variables to test complex hypotheses. The present study is a secondary data analysis of early (first three-month) Timeline Followback (TLFB) post-treatment drinking data from 200 problem drinkers who completed a short outpatient intervention. Using a growth mixture modeling (GMM) analysis, the goal was to identify different outcome drinking trajectories and examine the relationship between problem severity levels, treatment modality (i.e. individual versus group format), and goal choice (i.e. low-risk drinking versus abstinence) to those trajectories. Results demonstrated the existence of different outcome subgroups among problem drinkers. In addition, problem severity level was associated with outcomes and class membership. Observed significant differences in the relationships between predictor variables and specific outcome subgroups, and evidence of different drinking fluctuation patterns in the outcomes suggest that using a person-centered approach adds value beyond traditional statistical outcome analyses. The person-centered approach can facilitate the identification of relevant variables for patient-treatment matching hypotheses for problem drinkers.

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The maturation and consolidation of executive functions, including cognitive flexibility, attentional control, goal-setting, and information processing, continues throughout adolescence. Cognitive vulnerabilities to depression, such as rumination on negative affect, negative cognitive style, and hopelessness, also emerge as stable risk-factors for depression during this time. Emerging evidence suggests these vulnerabilities may be associated with alterations in executive functioning, and with cognitive maturation. The current study explores the association between trajectories of executive development and cognitive vulnerabilities to depression using a person-centered characterization of latent classes of growth trajectories. Classes of adolescent cognitive development in working memory, selective attention, sustained attention, switching, and divided attention, were derived, and class associations with cognitive vulnerabilities were probed. The results showed that most executive domains have a normative majority with typical growth and low levels of cognitive vulnerability. Minority classes, representing atypical growth, were differentially related to cognitive vulnerability. Contrary to hypotheses, better cognitive development was generally associated with higher levels of cognitive vulnerability, specifically internal, stable, and self-worth dimensions of negative cognitive style. Several exceptions included classes whose trajectory suggested developmental regression; consistent with hypotheses, these classes also demonstrated higher levels of negative cognitive style. Results support a model in which cognitive development scaffolds the maturation of negative cognitive style. / Psychology

Due to difficulties in elucidating neurobiological aspects of psychological disorders, the National Institute of Mental Health (NIMH) created the Research Domain Criteria (RDoC), which encourages novel conceptualizations of the relationship between neurobiological circuitry and clinical difficulties. This approach is markedly different from the Diagnostic and Statistical Manual of Mental Disorders (DSM) based approach that has dominated clinical research to date. Thus, RDoC necessitates exploration of novel experimental and statistical approaches. Fear learning paradigms represent a promising methodology for elucidating connections between acute threat (“fear”) circuitry and fear-related clinical difficulties. However, traditional analytical approaches rely on central tendency statistics, which are tethered to a priori categories and assume homogeneity within groups. Growth Mixture Modeling (GMM) methods such as Latent Class Growth Analysis (LCGA) may be uniquely suited for examining fear learning phenotypes. However, just three extant studies have applied GMM to fear learning and only one did so in a human population. Thus, the degree to which classes identified in known studies represent characteristics of the general population and to which GMM methodology is applicable across populations and paradigms is unclear. This preliminary study applied LCGA to a fear learning lab study in an attempt to identify heterogeneity in fear learning patterns based on a posteriori classification. The findings of this investigation may inform efforts to move toward a trans-diagnostic conceptualization of fear learning. Consistent with the goals laid out in RDoC, explication of fear learning phenotypes may eventually provide critical information needed to spur innovation in psychotherapeutic and psychopharmacological treatment. / Master of Science / To date, most clinical psychology research has been based on the Diagnostic and Statistical Manual of Mental Disorders (DSM), which is a catalog of mental health disorders that was originally designed to facilitate communication among clinicians. Many experts contend that this approach has hampered progress in the field of biological clinical psychology research. Thus, the National Institute of Mental Health (NIMH) created a new template for biological clinical psychology research called the Research Domain Criteria (RDoC). Since RDoC calls for a complete overhaul in the conceptualization of clinical dysfunction, this approach requires statistical and experimental innovation. One traditional experimental approach that may be helpful in understanding the RDoC topic of acute threat (“fear”) is called Pavlovian Fear Learning (PFL). However, traditional PFL studies have utilized statistical methods that are based on comparing group averages and require researchers to determine groups of interest based on theory before the study begins. This is problematic because RDoC calls for research that begins with evidence rather than theory. Growth Mixture Modeling (GMM) is a statistical methodology that may allow researchers to analyze fear learning data without having to begin with theoretically determined categories such as DSM disorders. However, little research has tested how well this approach would work. This study is just the second to apply a GMM approach to a human PFL study. The findings from this investigation may inform efforts to develop a statistical technique that is well suited for RDoCian research and may also spur innovation in psychotherapeutic and psychopharmacological treatment.

Doctor of Philosophy / Department of Family Studies and Human Services / Jared A. Durtschi and Sandra M. Stith / Early binge drinking (i.e., five or more drinks on a single occasion) is associated with a greater risk of later substance abuse or dependence, and other non-alcohol related problems in adulthood, (e.g., adult civil or criminal convictions). Identifying alcohol use trajectories has mainly been limited to within single developmental periods (i.e., adolescence or emerging adulthood) or between developmental periods up until around the legal drinking age. Using N = 1,864 adolescents from the National Longitudinal Study of Adolescent Health (Add Health) dataset, this paper sought to identify trajectories of binge drinking beginning in adolescence and into adulthood using growth mixture modeling. Family factors (e.g., parent-child communication, shared activities, connectedness, and parental control) were used to predict the various trajectories. Two class trajectories were identified, a low initial-escalating group (87%), and a high initial-deescalating group (13%). Being male and having more close friends using alcohol were predictive of a greater likelihood of being in the high initial-deescalating group. Results can inform therapeutic interventions in an effort to affect an adolescent’s trajectory of use and reduce the risk of long-term heavy alcohol use.