ANALYSIS OF ROLLING GROUP THERAPY DATA USING CONDITIONALLY AUTOREGRESSIVE PRIORS

Intervention effect of group counseling on social support and post-stress growth of orphans and vulnerable children in China

Orphans and vulnerable children fall under the category of children who are at risk of exposure to more stressful circumstances and receive less social assistance compared to other children. This study aims to investigate the impact of group counseling based on social intervention and psychological therapy (SSGPC) on social support and the perceived stress growth of orphans and vulnerable children. In one special educational needs school in Nanning, China, the SSGPC was developed and implemented. Using the social support and post-stress growth scales, the researchers investigated the effects of SSGPC on orphans and vulnerable children. Twenty-seven orphans and vulnerable children between the ages of nine and 12 were arbitrarily assigned to the experimental and control groups. A pre-test post-test method of quasi-experimental design was applied, with 13 participants in the experimental group and 14 in the control group. The results revealed that the intervention group had significantly higher scores for social support and post-stress growth than the control group. The SSGPC had significantly improved the levels of social support for orphans. The findings indicated that the SSGPC provided an effective way to improve social support and post-stress growth of orphans and vulnerable children.

Causal inference under interference with prognostic scores for dynamic group therapy studies

Group therapy is a common treatment modality for behavioral health conditions. Patients often enter and exit groups on an ongoing basis, leading to dynamic therapy groups. Examining the effect of high versus low session attendance on patient outcomes is a research question of interest. However, there are several challenges to identifying causal effects in this setting, including the lack of randomization, interference among patients, and the interrelatedness of patient participation. Dynamic therapy groups motivate a unique causal inference scenario, as the treatment statuses are completely defined by the patient attendance record for the therapy session, which is also the structure inducing interference. We adopt the Rubin causal model framework to define the causal effect of high versus low session attendance of group therapy at both the individual patient and peer levels. We propose a strategy to identify individual, peer, and total effects of high attendance versus low attendance on patient outcomes by the prognostic score stratification. We examine performance of our approach via simulation and apply it to data from a group cognitive behavioral therapy trial for treating depression among patients in a substance use disorders treatment setting.

Appropriate analyses of bimodal substance use frequency outcomes: a mixture model approach

Background: In addiction research, outcome measures are often characterized by bimodal distributions. One mode can be for individuals with low substance use and the other mode for individuals with high substance use. Applying standard statistical procedures to bimodal data may result in invalid inference. Mixture models are appropriate for bimodal data because they assume that the sampled population is composed of several underlying subpopulations.Objectives: To introduce a novel mixture modeling approach to analyze bimodal substance use frequency data.Methods: We reviewed existing models used to analyze substance use frequency outcomes and developed multiple alternative variants of a finite mixture model. We applied all methods to data from a randomized controlled study in which 30-day alcohol abstinence was the primary outcome. Study data included 73 individuals (38 men and 35 women). Models were implemented in the software packages SAS, Stata, and Stan.Results: Shortcomings of existing approaches include: 1) inability to model outcomes with multiple modes, 2) invalid statistical inferences, including anti-conservative p-values, 3) sensitivity of results to the arbitrary choice to model days of substance use versus days of substance abstention, and 4) generation of predictions outside the range of common substance use frequency outcomes. Our mixture model variants avoided all of these shortcomings.Conclusions: Standard models of substance use frequency outcomes can be problematic, sometimes overstating treatment effectiveness. The mixture models developed improve the analysis of bimodal substance use frequency.

Study protocol for a group-based motivational interviewing brief intervention to reduce substance use and sexual risk behavior among young adults experiencing homelessness

BACKGROUND

Young people experiencing homelessness have alarmingly high rates of alcohol and other drug (AOD) use, which is associated with sexual risk behaviors such as unprotected sex, trading sex, and sex with multiple casual partners. Few risk reduction programs for this population have been developed and rigorously evaluated, particularly those that address both of these interrelated behaviors, use a collaborative and non-judgmental approach, and are feasible to deliver in settings where homeless young people seek services. This paper describes the protocol of a study evaluating a four-session Motivational Interviewing (MI)-based group risk reduction intervention for this population. The protocol has been shown to be efficacious in pilot work over 3 months with 200 homeless young adults [1]. The current study seeks to refine the intervention protocol and evaluate the program on a larger scale.

METHODS/DESIGN

In a cluster-cross-over randomized controlled trial, 18-25 year olds will receive the AWARE risk reduction program (n = 200) or standard care (n = 200) at one of three drop-in centers serving homeless youth in the Los Angeles area. We will evaluate intervention effects on primary outcomes of AOD use and sexual risk behavior, as well as secondary outcomes of health-related quality of life and social stability, over a 12-month period.

DISCUSSION

This project has the potential to fill a significant gap in prevention services by demonstrating that a brief intervention, feasible to deliver within settings where young people experiencing homelessness typically seeks services, can significantly reduce the interrelated problems of AOD use and sexual risk behavior. Trial registration ClinicalTrials.gov Identifier: NCT03735784. Registered November 18, 2018, https://clinicaltrials.gov/ct2/show/record/NCT03735784 (retrospectively registered).

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

How group factors affect adolescent change talk and substance use outcomes: implications for motivational interviewing training

Clients who verbalize statements arguing for change (change talk [CT]) in psychotherapy are more likely to decrease alcohol and other drug use (AOD) compared with clients who voice statements in opposition of change (sustain talk [ST]). Little is known about how CT and ST are expressed in groups in which adolescents may vary in their AOD use severity and readiness to change. First, we examined how session content was associated with CT/ST, and then we looked at whether different subtypes of CT/ST were associated with subsequent AOD outcomes 3 months later. Audio recordings (N = 129 sessions) of a 6-session group motivational interviewing (MI) intervention, Free Talk, were coded. Session content was not associated with CT; however, some session content was associated with higher percentages of ST (e.g., normative feedback). Subtypes of CT (Commitment and Reason) were associated with improved AOD outcomes, whereas Ability subtype remarks were related to increased marijuana use, intentions, and consequences. Findings offer helpful guidance for clinical training and narrow in on the type of CT to try to elicit in Group MI sessions. Regardless of session content, adolescents can benefit from hearing CT during the group.

Evaluating Long-term Outcomes of NHS Stop Smoking Services (ELONS): a prospective cohort study

BACKGROUND

NHS Stop Smoking Services (SSSs) provide free at the point of use treatment for smokers who would like to stop. Since their inception in 1999 they have evolved to offer a variety of support options. Given the changes that have happened in the provision of services and the ongoing need for evidence on effectiveness, the Evaluating Long-term Outcomes for NHS Stop Smoking Services (ELONS) study was commissioned.

OBJECTIVES

The main aim of the study was to explore the factors that determine longer-term abstinence from smoking following intervention by SSSs. There were also a number of additional objectives.

DESIGN

The ELONS study was an observational study with two main stages: secondary analysis of routine data collected by SSSs and a prospective cohort study of service clients. The prospective study had additional elements on client satisfaction, well-being and longer-term nicotine replacement therapy (NRT) use.

SETTING

The setting for the study was SSSs in England. For the secondary analysis, routine data from 49 services were obtained. For the prospective study and its added elements, nine services were involved. The target population was clients of these services.

PARTICIPANTS

There were 202,804 cases included in secondary analysis and 3075 in the prospective study.

INTERVENTIONS

A combination of behavioural support and stop smoking medication delivered by SSS practitioners.

MAIN OUTCOME MEASURES

Abstinence from smoking at 4 and 52 weeks after setting a quit date, validated by a carbon monoxide (CO) breath test.

RESULTS

Just over 4 in 10 smokers (41%) recruited to the prospective study were biochemically validated as abstinent from smoking at 4 weeks (which was broadly comparable with findings from the secondary analysis of routine service data, where self-reported 4-week quit rates were 48%, falling to 34% when biochemical validation had occurred). At the 1-year follow-up, 8% of prospective study clients were CO validated as abstinent from smoking. Clients who received specialist one-to-one behavioural support were twice as likely to have remained abstinent than those who were seen by a general practitioner (GP) practice and pharmacy providers [odds ratio (OR) 2.3, 95% confidence interval (CI) 1.2 to 4.6]. Clients who received group behavioural support (either closed or rolling groups) were three times more likely to stop smoking than those who were seen by a GP practice or pharmacy providers (OR 3.4, 95% CI 1.7 to 6.7). Satisfaction with services was high and well-being at baseline was found to be a predictor of abstinence from smoking at longer-term follow-up. Continued use of NRT at 1 year was rare, but no evidence of harm from longer-term use was identified from the data collected.

CONCLUSIONS

Stop Smoking Services in England are effective in helping smokers to move away from tobacco use. Using the 52-week CO-validated quit rate of 8% found in this study, we estimate that in the year 2012-13 the services supported 36,249 clients to become non-smokers for the remainder of their lives. This is a substantial figure and provides one indicator of the ongoing value of the treatment that the services provide. The study raises a number of issues for future research including (1) examining the role of electronic cigarettes (e-cigarettes) in smoking cessation for service clients [this study did not look at e-cigarette use (except briefly in the longer-term NRT study) but this is a priority for future studies]; (2) more detailed comparisons of rolling groups with other forms of behavioural support; (3) further exploration of the role of practitioner knowledge, skills and use of effective behaviour change techniques in supporting service clients to stop smoking; (4) surveillance of the impact of structural and funding changes on the future development and sustainability of SSSs; and (5) more detailed analysis of well-being over time between those who successfully stop smoking and those who relapse. Further research on longer-term use of non-combustible nicotine products that measures a wider array of biomarkers of smoking-related harm such as lung function tests or carcinogen metabolites.

FUNDING

The National Institute for Health Research Health Technology Assessment programme. The UK Centre for Tobacco and Alcohol Studies provided funding for the longer-term NRT study.

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.

An introduction and integration of cross-classified, multiple membership, and dynamic group random-effects models

In longitudinal studies, time-varying group membership and group effects are important issues that need to be addressed. In this article we describe use of cross-classified and multiple membership random-effects models to address time-varying group membership, and dynamic group random-effects models to address time-varying group effects. We propose new models that integrate features of existing models, evaluate these models through simulation, provide guidance on how to fit these models, and apply the models in 2 real data examples. The discussion focuses on challenges in the application of these models.

Group motivational interviewing for adolescents: change talk and alcohol and marijuana outcomes

OBJECTIVE

Little is known about what may distinguish effective and ineffective group interventions. Group motivational interviewing (MI) is a promising intervention for adolescent alcohol and other drug use; however, the mechanisms of change for group MI are unknown. One potential mechanism is change talk, which is client speech arguing for change. The present study describes the group process in adolescent group MI and effects of group-level change talk on individual alcohol and marijuana outcomes.

METHOD

We analyzed 129 group session audio recordings from a randomized clinical trial of adolescent group MI. Sequential coding was performed with the Motivational Interviewing Skill Code (MISC) and the CASAA Application for Coding Treatment Interactions software application. Outcomes included past-month intentions, frequency, and consequences of alcohol and marijuana use; motivation to change; and positive expectancies.

RESULTS

Sequential analysis indicated that facilitator open-ended questions and reflections of change talk increased group change talk. Group change talk was then followed by more change talk. Multilevel models accounting for rolling group enrollment revealed group change talk was associated with decreased alcohol intentions, alcohol use, and heavy drinking 3 months later; group sustain talk was associated with decreased motivation to change, increased intentions to use marijuana, and increased positive alcohol and marijuana expectancies.

CONCLUSIONS

Facilitator speech and peer responses each had effects on change and sustain talk in the group setting, which were then associated with individual changes. Selective reflection of change talk in adolescent group MI is suggested as a strategy to manage group dynamics and increase behavioral change.

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++.

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.

Behavioural therapy for smoking cessation: the effectiveness of different intervention types for disadvantaged and affluent smokers

BACKGROUND

Disadvantaged smokers are less likely to be successful when trying to stop smoking than more affluent smokers. In the UK, NHS Stop Smoking Services (SSS) provide a range of pharmacotherapy and behavioural support, delivered by advisors with a range of backgrounds. Whether the types of support provided and who provides it influence differences in quit rates amongst low SES smokers compared with high SES smokers has not previously been examined.

METHODS

202,084 records of smokers in England who attended a NHS Stop Smoking Service between July 2010 and June 2011 were acquired. Smokers were followed-up by services at four weeks post quit date. Multilevel logistic regression models of CO validated quits were employed. Disadvantage was explored through the National Statistics Socio-Economic Classification (NS-SEC) and by eligibility for free prescriptions, an indicator of low income amongst adults aged between 19 and 59 in England.

RESULTS

Affluent smokers were more likely to quit than disadvantaged smokers (OR 1.38 (1.35 to 1.42) for clients who paid for prescriptions compared to those eligible for free prescriptions). 80% of service clients received one-to-one counselling but open group forms of behavioural therapy were more successful (main effect OR 1.26 (1.12 to 1.41)) except amongst some of the most disadvantaged clients (long-term unemployed and prisoners). Closed groups were little deployed and they were not significantly more successful than one-to-one behavioural therapy after controls. Who delivered treatment did make a difference for some clients, with all but the most affluent less likely to be successful if they had been treated by a nurse compared with other types of advisers, including smoking cessation specialists (main effect OR 0.73 (0.65 to 0.83)).

CONCLUSION

This study provides further evidence that disadvantaged smokers find quitting more difficult even when they have attended a smoking cessation programme. The findings suggest that open groups should be promoted, although they may not be as effective as other forms of behavioural therapy for the long-term unemployed or prisoners. Further research is required to explore why most groups of smokers who attended services staffed by nurses were less likely to quit than those who received treatment from other types of advisors.

A randomized controlled trial of a group motivational interviewing intervention for adolescents with a first time alcohol or drug offense

Group motivational interviewing (MI) interventions that target youth at-risk for alcohol and other drug (AOD) use may prevent future negative consequences. Youth in a teen court setting [n=193; 67% male, 45% Hispanic; mean age 16.6 (SD=1.05)] were randomized to receive either a group MI intervention, Free Talk, or usual care (UC). We examined client acceptance, and intervention feasibility and conducted a preliminary outcome evaluation. Free Talk teens reported higher quality and satisfaction ratings, and MI integrity scores were higher for Free Talk groups. AOD use and delinquency decreased for both groups at 3 months, and 12-month recidivism rates were lower but not significantly different for the Free Talk group compared to UC. Results contribute to emerging literature on MI in a group setting. A longer term follow-up is warranted.

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

There are several challenges to testing the effectiveness of group therapy-based interventions in alcohol and other drug use (AOD) treatment settings. Enrollment into AOD therapy groups typically occurs on an open (rolling) basis. Changes in therapy group membership induce a complex correlation structure among client outcomes, with relatively small numbers of clients attending each therapy group session. Primary outcomes are measured post-treatment, so each datum reflects the effect of all sessions attended by a client. The number of post-treatment outcomes assessments is typically very limited. The first feature of our modeling approach relaxes the assumption of independent random effects in the standard multiple membership model by employing conditional autoregression (CAR) to model correlation in random therapy group session effects associated with clients' attendance of common group therapy sessions. A second feature specifies a longitudinal growth model under which the posterior distribution of client-specific random effects, or growth parameters, is modeled non-parametrically. The Dirichlet process prior helps to overcome limitations of standard parametric growth models given limited numbers of longitudinal assessments. We motivate and illustrate our approach with a data set from a study of group cognitive behavioral therapy to reduce depressive symptoms among residential AOD treatment clients.

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.

Analyzing repeated measures data on individuals nested within groups: accounting for dynamic group effects

Researchers commonly collect repeated measures on individuals nested within groups such as students within schools, patients within treatment groups, or siblings within families. Often, it is most appropriate to conceptualize such groups as dynamic entities, potentially undergoing stochastic structural and/or functional changes over time. For instance, as a student progresses through school, more senior students matriculate while more junior students enroll, administrators and teachers may turn over, and curricular changes may be introduced. What it means to be a student within that school may thus differ from 1 year to the next. This article demonstrates how to use multilevel linear models to recover time-varying group effects when analyzing repeated measures data on individuals nested within groups that evolve over time. Two examples are provided. The 1st example examines school effects on the science achievement trajectories of students, allowing for changes in school effects over time. The 2nd example concerns dynamic family effects on individual trajectories of externalizing behavior and depression.

Evaluation of a drop-in rolling-group model of support to stop smoking

AIMS

To assess longer-term outcomes of a drop-in rolling-group model of behavioural support for smoking cessation and the factors that influence cessation outcomes.

DESIGN

Prospective observational cohort study.

SETTING

Fag Ends NHS Stop Smoking Service in Liverpool and Knowsley, UK.

PARTICIPANTS

A total of 2585 clients, aged 16 or over, setting a quit date.

MEASUREMENTS

Routine monitoring data were collected from Fag Ends service users and were supplemented by survey data on socio-economic circumstances, smoking-related behaviour and self-report and carbon monoxide (CO)-validated smoking status at 52-week follow-up.

FINDINGS

The CO-validated prolonged abstinence rate at 52 weeks for smokers attending the groups was 5.6%, compared with 30.7% at 4 weeks (a relapse rate of 78.2%). The sample was particularly disadvantaged: 68% resided in the most deprived decile of the English Index of Multiple Deprivation. Higher socio-economic status within the sample was a predictor of quitting. Other predictors of long-term cessation in multivariate analysis included older age, being female, lower levels of nicotine dependence, having a live-in partner, stronger determination to quit and use of varenicline versus other medication.

CONCLUSIONS

A wholly state-reimbursed clinical stop-smoking service providing behavioural support and medication in a region of high economic and social disadvantage has reached a significant proportion of the smoking population. Long-term success rates are lower than are found typically in clinical trials, but higher than would be expected if the smokers were to try and quit unaided. Research is needed into how to improve on the success rates achieved.