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Objective: This study uses data from the largest effectiveness trial to date on treatment of co-occurring posttraumatic stress and substance use disorders, using advances in statistical methodology for modeling treatment attendance and membership turnover in rolling groups. Method: Women receiving outpatient substance abuse treatment (N = 353) were randomized to 12 sessions of Seeking Safety or a health education control condition. Assessments were completed at baseline and at 1 week, 3, 6, and 12 months posttreatment. Outcome measures were alcohol and cocaine use in the prior 30 days captured using the Addiction Severity Index. Latent class pattern mixture modeling (LCPMM) was used to estimate attendance patterns and to test for treatment effects within and across latent attendance patterns and group membership turnover. Results: Across LCPMM analyses for alcohol and cocaine use, similar treatment attendance patterns emerged: Completers never decreased below an 80% probability of attendance, droppers never exceeded a 41% probability of attendance, and titrators demonstrated a 50% to 80% probability of attendance. Among completers, there were significant decreases in alcohol use from baseline to 1-week posttreatment, followed by nonsignificant increases in alcohol during follow-up. No differences between treatment conditions were detected. Titrators in Seeking Safety had lower rates of alcohol use from 1-week through 12-month follow-up compared with control participants. Droppers had nonsignificant increases in alcohol during both study phases. Cocaine use findings were similar but did not reach significance levels. Conclusions: The impact of client self-modulation of treatment dosage and group membership composition may influence behavioral treatment outcomes among this population. (Contains 1 footnote, 3 tables, and 4 figures.)

Interventions for a variety of emotional and behavioral problems are commonly delivered in the context of treatment groups, with many using rolling admission to sustain membership (i.e., admission, dropout, and discharge from group are perpetual and ongoing). The authors present an overview of the analytic challenges inherent in rolling group data and outline commonly used (but flawed) analytic and design approaches to addressing (or sidestepping) these issues. Moreover, the authors propose use of latent class pattern mixture models (LCPMMs) as a statistically and conceptually defensible approach for modeling treatment data from rolling groups. The LCPMM approach is illustrated with rolling group data from a group-based alcoholism pilot treatment trial (N = 128). Different inferences were made with regard to treatment efficacy under LCPMM vs. the commonly used standard group-clustered latent growth model (LGM); coupled with other preliminary findings in this area, inferences from LGMs may be overly liberal when applied to data from rolling groups. Continued work on data analytic difficulties in groups with membership turnover is critical for furthering the ecological validity of research on behavioral treatments.

It is unknown whether perceived pubertal timing changes as puberty progresses or whether it is an important component of adolescent identity formation that is fixed early in pubertal development. The purpose of this study is to examine the stability of perceived pubertal timing among a school-based sample of rural adolescents aged 11-17 ( N = 6,425; 50% female; 53% White). Two measures of pubertal timing were used, stage- normative, based on the Pubertal Development Scale, a self-report scale of secondary sexual characteristics, and peer- normative, a one-item measure of perceived pubertal timing. Two longitudinal methods were used: one-way random effects ANOVA models and latent class analysis. When calculating intraclass correlation coefficients using the one-way random effects ANOVA models, which is based on the average reliability from one time point to the next, both measures had similar, but poor, stability. In contrast, latent class analysis, which looks at the longitudinal response pattern of each individual and treats deviation from that pattern as measurement error, showed three stable and distinct response patterns for both measures: always early, always on-time, and always late. Study results suggest instability in perceived pubertal timing from one age to the next, but this instability is likely due to measurement error. Thus, it may be necessary to take into account the longitudinal pattern of perceived pubertal timing across adolescence rather than measuring perceived pubertal timing at one point in time. [ABSTRACT FROM AUTHOR]

Abstract: Modeling turnover in group membership has been identified as a key barrier contributing to a disconnect between the manner in which behavioral treatment is conducted (open-enrollment groups) and the designs of substance abuse treatment trials (closed-enrollment groups, individual therapy). Latent class pattern mixture models (LCPMMs) are emerging tools for modeling data from open-enrollment groups with membership turnover in recently proposed treatment trials. The current article illustrates an approach to conducting power analyses for open-enrollment designs based on the Monte Carlo simulation of LCPMM models using parameters derived from published data from a randomized controlled trial comparing Seeking Safety to a Community Care condition for women presenting with comorbid posttraumatic stress disorder and substance use disorders. The example addresses discrepancies between the analysis framework assumed in power analyses of many recently proposed open-enrollment trials and the proposed use of LCPMM for data analysis. [ABSTRACT FROM AUTHOR]

Abstract: Historically, difficulties in analyzing treatment outcome data from open-enrollment groups have led to their avoidance in use in federally funded treatment trials despite the fact that 79% of treatment programs use open-enrollment groups. Recently, latent class pattern mixture models (LCPMM) have shown promise as a defensible approach for making overall (and attendance-class-specific) inferences from open-enrollment groups with membership turnover. We present a statistical simulation study comparing LCPMMs to longitudinal growth models (LGM) to understand when both frameworks are likely to produce conflicting inferences concerning overall treatment efficacy. LCPMMs performed well under all conditions examined; meanwhile, LGMs produced problematic levels of bias and Type I errors under two joint conditions: moderate to high dropout (30%–50%) and treatment by attendance class interactions exceeding Cohen''s d ≈ .2. This study highlights key concerns about using LGM for open-enrollment data: treatment effect overestimation and advocacy for treatments that may be ineffective in reality. [Copyright &y& Elsevier]

Abstract: Difficulties in modeling turnover in treatment-group membership have been cited as one of the major impediments to ecological validity of substance abuse and alcoholism treatment research. In this review, our primary foci are on (a) the discussion of approaches that draw on state-of-the-science analytic methods for modeling open-enrollment group data and (b) highlighting emerging issues that are critical to this relatively new area of methodological research (e.g., quantifying membership change, modeling “holiday” effects, and modeling membership change among group members and leaders). Continuing refinement of new modeling tools to address these analytic complexities may ultimately lead to the development of more federally funded open-enrollment trials. These developments may also facilitate the building of a “community-friendly” treatment research portfolio for funding agencies that support substance abuse and alcoholism treatment research. [Copyright &y& Elsevier]