Timothy J. Ozechowski

The current analysis demonstrates the use of empirical Bayes (EB) estimation methods with data-derived prior parameters for studying clinically intricate process-mechanism-outcome linkages using structural equation modeling (SEM) with small samples. The data were obtained from a small subsample of 23 families receiving Functional Family Therapy (FFT) for adolescent substance abuse during a completed randomized clinical trial. Two or 3 video-recorded FFT sessions were randomly selected for each family. The middle 20-min portion of each session was observed and coded. An SEM examining the influence of a select set of observed therapist behaviors on pre- to posttreatment change in mother reports of family functioning and, in turn, pre- to posttreatment change in adolescent reports of adolescent marijuana use and delinquent behavior was specified. The SEM was implemented using EB estimation with data-derived maximum likelihood (ML) prior parameters and Markov Chain Monte Carlo (MCMC) estimation of the joint posterior distribution. The EB SEM results indicated that a relatively high proportion of individually focused general interventions (i.e., seek information, acknowledge) as well as relationally focused meaning change interventions by therapists during sessions of FFT were predictive of pre- to posttreatment increases in levels of family functioning as reported by mothers in families of substance-abusing adolescents. In turn, increases in mother-reported family functioning were predictive of reductions in levels of adolescent-reported delinquent behavior. EB MCMC methods produced more stable results than did ML, especially regarding the variances on the change factors in the SEM. EB MCMC estimation is a viable alternative to ML estimation of SEMs in clinical research with prohibitively small samples.

In any given year, only about 10% of the nearly two million adolescents exhibiting substance abuse or dependence in the United States receive substance abuse treatment. Given this state of affairs, it is unlikely that the massive effort and expenditure of resources over the past decade on developing, testing, and disseminating effective treatments for adolescent substance abuse will have an appreciable impact on the prevalence of substance use disorders among the adolescent population. In order to substantially diminish the pervasive gap between levels of need for and utilization of adolescent substance abuse treatment, specialized assertive outreach strategies may be needed. This paper outlines a framework for assertive outreach for adolescents with substance use disorders and proposes specific types of strategies for identifying and enrolling such adolescents into treatment. Implications for practice and policy pertaining to adolescent substance abuse treatment service delivery are considered.

This article demonstrates the use of mixed-effects logistic regression (MLR) for conducting sequential analyses of binary observational data. MLR is a special case of the mixed-effects logit modeling framework, which may be applied to multicategorical observational data. The MLR approach is motivated in part by G. A. Dagne, G. W. Howe, C. H. Brown, & B. O. Muthén (2002) advances in general linear mixed models for sequential analyses of observational data in the form of contingency table frequency counts. The advantage of the MLR approach is that it circumvents obstacles in the estimation of random sampling error encountered using Dagne and colleagues' approach. This article demonstrates the MLR model in an analysis of observed sequences of communication in a sample of young adult same-sex peer dyads. The results obtained using MLR are compared with those of a parallel analysis using Dagne and colleagues' linear mixed model for binary observational data in the form of log odds ratios. Similarities and differences between the results of the 2 approaches are discussed. Implications for the use of linear mixed models versus mixed-effects logit models for sequential analyses are considered.

Adolescent Substance Abuse - March 2006

This book was first published in 2006. Adolescent drug abuse is one of the most challenging disorders to treat. It impacts on schools, community-based programs, mental health and medical facilities, and juvenile justice settings. This book provides practitioners, program developers and policy makers with practical information for improving outcomes in adolescent substance abuse. The authors cover a range of issues, including empirically based treatment development protocols, how to incorporate innovative treatment models into diverse clinical settings; research advances; interventions with special populations, culturally based intervention guidelines, and recommendations for practice and policy.