Bayesian Hierarchical Semiparametric Modelling of Longitudinal Post-treatment Outcomes from Open Enrolment Therapy Groups

Can we predict adolescent cannabis use? A Bayesian semi-parametric approach to project future trends

Despite its decrease in many Western countries, cannabis remains the most used illicit substance among adolescents. This study aims to summarize cannabis consumption during the last two decades and project trends among 15-year-olds in the 2021-22 HBSC survey. A Bayesian semi-parametric hierarchical model was adopted to estimate the trend of cannabis consumption using data of about 287,000 adolescents from the 2001/2002 to the 2017/2018 HBSC wave and the 38 countries that met the inclusion criteria. Data show an overall decline in most countries for both boys and girls. However, in 22 countries of 38 cannabis use is expected to increase again in our projection. The discussion of these findings should take into account cultural, policy, social factors and unpredictable events such as the Covid-19 pandemic, that can significantly impact future trends leading to discrepancies between the projected and observed values. However, these discrepancies can provide insight into understanding the potential impact of preventive strategies and the underlying processes responsible for changes in cannabis use over time.

A Bayesian approach to model individual differences and to partition individuals: case studies in growth and learning curves

The first objective of the paper is to implement a two stage Bayesian hierarchical nonlinear model for growth and learning curves, particular cases of longitudinal data with an underlying nonlinear time dependence. The aim is to model simultaneously individual trajectories over time, each with specific and potentially different characteristics, and a time-dependent behavior shared among individuals, including eventual effect of covariates. At the first stage inter-individual differences are taken into account, while, at the second stage, we search for an average model. The second objective is to partition individuals into homogeneous groups, when inter individual parameters present high level of heterogeneity. A new multivariate partitioning approach is proposed to cluster individuals according to the posterior distributions of the parameters describing the individual time-dependent behaviour. To assess the proposed methods, we present simulated data and two applications to real data, one related to growth curve modeling in agriculture and one related to learning curves for motor skills. Furthermore a comparison with finite mixture analysis is shown.

Forecasting Frequent Alcohol Use among Adolescents in HBSC Countries: A Bayesian Framework for Making Predictions

(1) Aim: To summarize alcohol trends in the last 30 years (1985/6–2017/8) among 15-year-olds in Health Behaviour in School-aged Children (HBSC) countries (overall sample size: 413,399 adolescents; 51.55% girls) and to forecast the potential evolution in the upcoming 2021/22 HBSC survey. (2) Methods: Using 1986–2018 prevalence data on weekly alcohol consumption among 15-year-olds related to 40 HBSC countries/regions, a Bayesian semi-parametric hierarchical model was adopted to estimate trends making a clusterization of the countries, and to give estimates for the 2022 HBSC survey. (3) Results: An overall declining trend in alcohol consumption was observed over time in almost all the countries. However, compared to 2014, some countries showed a new increase in 2018 and 2021/22 estimates forecast a slight increase in the majority of countries, pointing out a potential bounce after a decreasing period in frequent drinking habits. (4) Conclusions: The clusterization suggested a homogenization of consumption habits among HBSC countries. The comparison between 2022 observed and expected data could be helpful to investigate the effect of risk behaviour determinants, including the pandemic impact, occurring between the last two waves of the survey.

Randomized Clinical Trial Examining Cognitive Behavioral Therapy for Individuals With a First-Time DUI Offense

BACKGROUND

Driving under the influence (DUI) programs are a unique setting to reduce disparities in treatment access to those who may not otherwise access treatment. Providing evidence-based therapy in these programs may help prevent DUI recidivism.

METHODS

We conducted a randomized clinical trial of 312 participants enrolled in 1 of 3 DUI programs in California. Participants were 21 and older with a first-time DUI offense who screened positive for at-risk drinking in the past year. Participants were randomly assigned to a 12-session manualized cognitive behavioral therapy (CBT) or usual care (UC) group and then surveyed 4 and 10 months later. We conducted intent-to-treat analyses to test the hypothesis that participants receiving CBT would report reduced impaired driving, alcohol consumption (drinks per week, abstinence, and binge drinking), and alcohol-related negative consequences. We also explored whether race/ethnicity and gender moderated CBT findings.

RESULTS

Participants were 72.3% male and 51.7% Hispanic, with an average age of 33.2 (SD = 12.4). Relative to UC, participants receiving CBT had lower odds of driving after drinking at the 4- and 10-month follow-ups compared to participants receiving UC (odds ratio [OR] = 0.37, p = 0.032, and OR = 0.29, p = 0.065, respectively). This intervention effect was more pronounced for females at 10-month follow-up. The remaining 4 outcomes did not significantly differ between UC versus CBT at 4- and 10-month follow-ups. Participants in both UC and CBT reported significant within-group reductions in 2 of 5 outcomes, binge drinking and alcohol-related consequences, at 10-month follow-up (p < 0.001).

CONCLUSIONS

In the short-term, individuals receiving CBT reported significantly lower rates of repeated DUI than individuals receiving UC, which may suggest that learning cognitive behavioral strategies to prevent impaired driving may be useful in achieving short-term reductions in impaired driving.

Group Cohesion and Climate in Cognitive Behavioral Therapy for Individuals with a First-Time DUI

Few studies have examined group cohesion and climate in the substance use disorder treatment literature. We examined whether group cohesion and climate are associated with increased self-efficacy outcomes and reduced drinks per week, binge drinking and DUI behaviors, in a sample of individuals with a first-time DUI receiving either cognitive behavioral therapy (CBT) or usual care. Additionally, we examined whether CBT moderates these relationships. Group measures and drinking outcomes were not significantly associated. This study is the first to provide an in-depth analysis on group processes in DUI settings, and as such, provides important insights into how group processes may differ in a mandated DUI context.

Bayesian models for semicontinuous outcomes in rolling admission therapy groups

Alcohol and other drug abuse are frequently treated in a group therapy setting. If participants are allowed to enroll in therapy on a rolling basis, irregular patterns of participant overlap can induce complex correlations of participant outcomes. Previous work has accounted for common session attendance by modeling random effects for each therapy session, which map to participant outcomes via a multiple membership construction when modeling normally distributed outcome measures. We build on this earlier work by extending the models to semicontinuous outcomes, or outcomes that are a mixture of continuous and discrete distributions. This results in multivariate session effects, for which we allow temporal dependencies of various orders. We illustrate our methods using data from a group-based intervention to treat substance abuse and depression, focusing on the outcome of average number of drinks per day. Alcohol and other drug abuse are frequently treated in a group therapy setting. If 2 clients attend the some of the same sessions, we might expect that-on average-their posttreatment outcomes would be more similar than if they had not attended any sessions together. Hence, if participants are allowed to enroll in therapy on a rolling basis, irregular patterns of session attendance can induce complex relationships between participant outcomes. Statistical methods have been developed previously to account for rolling admission group therapy when the outcomes are normally distributed. In the case of alcohol and other drug use interventions, however, a substantial fraction of participants often report zero use after treatment. We extend previous work to build models that accommodate semicontinuous outcomes, which are a mixture of continuous and discrete distributions, for such situations. We find that modern Bayesian statistical methods and software allow users to efficiently estimate nonstandard models such as these. We illustrate our methods using data from a group-based intervention to treat substance abuse and depression, focusing on the outcome of average number of drinks per day. We find that the intervention is associated with a drop in the probability of any drinking, but find no evidence of a change in the amount of drinking, conditional on some drinking. (PsycINFO Database Record

Group motivational interviewing for homeless young adults: Associations of change talk with substance use and sexual risk behavior

Homeless young adults exhibit high rates of alcohol and other drug (AOD) use and sexual risk behaviors. This study is a secondary analysis of data collected in a randomized clinical trial of AWARE, a new 4 session group motivational interviewing intervention. AWARE mainly focused on alcohol use and sexual risk behavior given focus group feedback. We used sequential coding to analyze how the group process affected both AOD use and sexual risk behavior at 3-month follow up among homeless young adults by examining facilitator behavior and participant change talk (CT) and sustain talk (ST). We analyzed 57 group session digital recordings of 100 youth (69% male, 74% heterosexual, 28% non-Hispanic white, 23% African American, 26% Hispanic, 23% multiracial/other; mean age 21.75). Outcomes included importance and readiness to change AOD use and risky sexual behavior, AOD use and consequences, number of partners and unprotected sex, and condom self-efficacy. Sequential analysis indicated that facilitator open-ended questions and reflections of CT increased Group CT. Group CT was associated with a lower likelihood of being a heavy drinker 3 months later; Group ST was associated with decreased readiness and confidence to change alcohol use. There were no associations with CT or ST for drug use or risky sexual behavior. Facilitator speech and peer responses were related to CT and ST during the group sessions with this high risk population, which were then associated with individual changes for alcohol use. Further research is needed to explore associations with drug use and sexual risk behavior. (PsycINFO Database Record

A group-based motivational interviewing brief intervention to reduce substance use and sexual risk behavior among homeless young adults

Homeless young adults ages 18-25 exhibit high rates of alcohol and other drug (AOD) use, and sexual risk behaviors such as unprotected sex. Yet few programs exist for this population that are both effective and can be easily incorporated into settings serving this population. This pilot cluster cross-over randomized controlled trial evaluates AWARE, a voluntary four session group-based motivational interviewing (MI) intervention to reduce AOD use and sexual risk behavior. We evaluated AWARE with 200 homeless young adults using drop-in center services in Los Angeles County (mean age=21.8years; 73% male; 79% heterosexual; 31% non-Hispanic White, 25% African American, 24% Hispanic, 21% multiracial/other). Surveys were completed at baseline and three months after program completion. Retention in the AWARE program was excellent (79% attended multiple sessions) and participants reported high levels of satisfaction with the program. AWARE participants self-reported positive change in their past 3month and past 30day alcohol use (ps≤0.05), motivation to change drug use (ps<0.05), and condom use self-efficacy (p=0.05) compared to the control group. Among those with multiple sex partners, AWARE participants showed a decrease in unprotected sexual events (p<0.05), whereas the control group did not. Results from this pilot evaluation are promising, suggesting that a brief group-MI risk reduction intervention can be effective in helping homeless young adults make positive changes in their alcohol and condom use. Further work is needed to more fully evaluate the efficacy of AWARE on AOD behavior and sexual risk behavior outcomes.

Bayesian restricted spatial regression for examining session features and patient outcomes in open-enrollment group therapy studies

Group-based interventions have been developed for treating patients across a range of health conditions. Enrollment into such groups often occurs on an open (or rolling) basis. Conditional autoregression modeling of random session effects has been proposed to account for the expected correlation in session effects associated with the overlap in patient participation session to session. However, when the analytic objective is to examine the relationship between a fixed-effect session feature and a patient outcome using conditional autoregression, confounding might arise if the fixed session feature of interest and the random session effects vary across sessions in similar ways, resulting in bias and inflated standard errors of a fixed-effect session feature of interest. Motivated by the goal of examining the relationships between outcomes and the session features of leader and session module theme, we applied restricted spatial regression to the analysis of patient outcomes collected from 132 participants in an open-enrollment group for treating depression among patients of a residential alcohol and other drug treatment program, adapting the approach to the multilevel data structure of open-enrollment group data. As compared with standard conditional autoregression, the restricted regression approach resulted in more precise estimates of regression coefficients of the module theme and leader predictor variables. The restricted regression approach provides an important analytic tool for group therapy researchers who are investigating the relationship between key components of open-enrollment group therapy interventions and patient outcomes.

Bayesian Semi- and Non-parametric Models for Longitudinal Data with Multiple Membership Effects in R

We introduce growcurves for R that performs analysis of repeated measures multiple membership (MM) data. This data structure arises in studies under which an intervention is delivered to each subject through the subject's participation in a set of multiple elements that characterize the intervention. In our motivating study design under which subjects receive a group cognitive behavioral therapy (CBT) treatment, an element is a group CBT session and each subject attends multiple sessions that, together, comprise the treatment. The sets of elements, or group CBT sessions, attended by subjects will partly overlap with some of those from other subjects to induce a dependence in their responses. The growcurves package offers two alternative sets of hierarchical models: 1. Separate terms are specified for multivariate subject and MM element random effects, where the subject effects are modeled under a Dirichlet process prior to produce a semi-parametric construction; 2. A single term is employed to model joint subject-by-MM effects. A fully non-parametric dependent Dirichlet process formulation allows exploration of differences in subject responses across different MM elements. This model allows for borrowing information among subjects who express similar longitudinal trajectories for flexible estimation. growcurves deploys "estimation" functions to perform posterior sampling under a suite of prior options. An accompanying set of "plot" functions allow the user to readily extract by-subject growth curves. The design approach intends to anticipate inferential goals with tools that fully extract information from repeated measures data. Computational efficiency is achieved by performing the sampling for estimation functions using compiled C++.

Effectiveness of the Treatment Readiness and Induction Program for increasing adolescent motivation for change

Success in substance abuse treatment is improved by problem recognition, desire to seek help, and readiness to engage in treatment, all of which are important aspects of motivation. Interventions that facilitate these at treatment induction for adolescents are especially needed. The purpose of this study is to assess the effectiveness of TRIP (Treatment Readiness and Induction Program) in promoting treatment motivation. Data represent 519 adolescents from 6 residential programs who completed assessments at treatment intake (time 1) and 35 days after admission (time 2). The design consisted of a comparison sample (n=281) that had enrolled in treatment prior to implementation of TRIP (standard operating practice) and a sample of clients that had entered treatment after TRIP began and received standard operating practice enhanced by TRIP (n=238). Repeated measures ANCOVAs were conducted using each time 2 motivation scale as a dependent measure. Motivation scales were conceptualized as representing sequential stages of change. LISREL was used to test a structural model involving TRIP participation, gender, drug use severity, juvenile justice involvement, age, race-ethnicity, prior treatment, and urgency as predictors of the stages of treatment motivation. Compared to standard practice, adolescents receiving TRIP demonstrated greater gains in problem recognition, even after controlling for the other variables in the model. The model fit was adequate, with TRIP directly affecting problem recognition and indirectly affecting later stages of change (desire for help and treatment readiness). Future studies should examine which specific components of TRIP affect change in motivation.

Does group cognitive-behavioral therapy module type moderate depression symptom changes in substance abuse treatment clients?

Little is known about the effect of group therapy treatment modules on symptom change during treatment and on outcomes post-treatment. Secondary analyses of depressive symptoms collected from two group therapy studies conducted in substance use treatment settings were examined (n=132 and n=44). Change in PHQ-9 scores was modeled using longitudinal growth modeling combined with random effects modeling of session effects, with time-in-treatment interacted with module theme to test moderation. In both studies, depressive symptoms significantly decreased during the active treatment phase. Symptom reductions were not significantly moderated by module theme in the larger study. However, the smaller pilot study's results suggest that future examination of module effects is warranted, given the data are compatible with differential reductions in reported symptoms being associated with attending people-themed module sessions versus thoughts-themed sessions.

The cost-effectiveness of depression treatment for co-occurring disorders: a clinical trial

The authors aimed to determine the economic value of providing on-site group cognitive behavioral therapy (CBT) for depression to clients receiving residential substance use disorder (SUD) treatment. Using a quasi-experimental design and an intention-to-treat analysis, the incremental cost-effectiveness and cost-utility ratio of the intervention were estimated relative to usual care residential treatment. The average cost of a treatment episode was $908, compared to $180 for usual care. The incremental cost effectiveness ratio was $131 for each point improvement of the BDI-II and $49 for each additional depression-free day. The incremental cost-utility ratio ranged from $9,249 to $17,834 for each additional quality adjusted life year. Although the intervention costs substantially more than usual care, the cost effectiveness and cost-utility ratios compare favorably to other depression interventions. Health care reform should promote dissemination of group CBT to individuals with depression in residential SUD treatment.

Bayesian Non-Parametric Hierarchical Modeling for Multiple Membership Data in Grouped Attendance Interventions

We develop a dependent Dirichlet process (DDP) model for repeated measures multiple membership (MM) data. This data structure arises in studies under which an intervention is delivered to each client through a sequence of elements which overlap with those of other clients on different occasions. Our interest concentrates on study designs for which the overlaps of sequences occur for clients who receive an intervention in a shared or grouped fashion whose memberships may change over multiple treatment events. Our motivating application focuses on evaluation of the effectiveness of a group therapy intervention with treatment delivered through a sequence of cognitive behavioral therapy session blocks, called modules. An open-enrollment protocol permits entry of clients at the beginning of any new module in a manner that may produce unique MM sequences across clients. We begin with a model that composes an addition of client and multiple membership module random effect terms, which are assumed independent. Our MM DDP model relaxes the assumption of conditionally independent client and module random effects by specifying a collection of random distributions for the client effect parameters that are indexed by the unique set of module attendances. We demonstrate how this construction facilitates examining heterogeneity in the relative effectiveness of group therapy modules over repeated measurement occasions.