Dimensional Assessment of Restricted and Repetitive Behaviors: Development and Preliminary Validation of a New Measure.

OBJECTIVE: This study aimed to provide initial validation of the Dimensional Assessment of Restricted and Repetitive Behaviors (DARB), a new parent-report measure designed to capture the full range of key restricted and repetitive behaviors (RRB) subdomains. METHOD: Parents of 1,892 children and adolescents with autism spectrum disorder (mean [SD] age = 10.81 [4.14] years) recruited from the SPARK (Simons Foundation Powering Autism Research for Knowledge) research match completed the DARB, several existing RRB instruments, and measures of social and communication impairments and anxiety. A subsample of 450 parents completed the DARB after 2 weeks to evaluate the test-retest stability. RESULTS: Exploratory graph analysis conducted in the exploratory subsample identified 8 dimensions that were aligned with hypothesized RRB subdomains: repetitive sensory motor behaviors, insistence on sameness, restricted interests, unusual interests, sensory sensitivity, self-injurious behaviors, obsessions and compulsive behaviors, and repetitive language. The confirmatory application of the exploratory structural equation modeling conducted in the confirmatory subsample showed that the derived factor structure had a good fit to the data. Derived factors had excellent reliability, convergent and divergent validity, and very strong test-retest stability and showed a distinct pattern of associations with key demographic, cognitive and clinical correlates. CONCLUSION: The DARB will be useful in a variety of research and clinical contexts considering the prominence and clinical impact of RRB in autism spectrum disorder. Strong preliminary evidence indicates that the new scale is comprehensive and captures a wide range of distinct RRB subdomains not simultaneously captured by any of the existing instruments.

Fine Particulate Air Pollution, Early Life Stress, and Their Interactive Effects on Adolescent Structural Brain Development: A Longitudinal Tensor-Based Morphometry Study.

Air pollution is a major environmental threat to public health; we know little, however, about its effects on adolescent brain development. Exposure to air pollution co-occurs, and may interact, with social factors that also affect brain development, such as early life stress (ELS). Here, we show that severity of ELS and fine particulate air pollution (PM2.5) are associated with volumetric changes in distinct brain regions, but also uncover regions in which ELS moderates the effects of PM2.5. We interviewed adolescents about ELS events, used satellite-derived estimates of ambient PM2.5 concentrations, and conducted longitudinal tensor-based morphometry to assess regional changes in brain volume over an approximately 2-year period (N = 115, ages 9-13 years at Time 1). For adolescents who had experienced less severe ELS, PM2.5 was associated with volumetric changes across several gray and white matter regions. Fewer effects of PM2.5 were observed for adolescents who had experienced more severe ELS, although occasionally they were in the opposite direction. This pattern of results suggests that for many brain regions, moderate to severe ELS largely constrains the effects of PM2.5 on structural development. Further theory and research is needed on the joint effects of ELS and air pollution on the brain.

Training models for implementing evidence-based psychological treatment for college mental health: A cluster randomized trial study protocol.

Mental disorders often emerge in adolescence and young adulthood, and these disorders can have lasting effects on students' health, social functioning, and education. Although evidence-based treatments have been established for many mental disorders, few community therapists use such treatments. What is needed is a practical, economically feasible means of training clinicians to implement evidence-based treatments suitable for widespread use. This cluster randomized trial will randomize 26 college counseling centers to one of two implementation strategies for training counselors to use interpersonal psychotherapy (IPT), an evidence-based treatment for depression and eating disorders: 1) an external expert consultation model comprising a workshop, therapy manual, and expert follow-up consultation (n = 13); or 2) a train-the-trainer model in which a staff member from the counseling center is coached to train other staff members to implement IPT (n = 13). The primary outcome is therapist adherence to IPT, with secondary outcomes of therapist competence in IPT and client outcomes for depression and eating disorders. Therapist and organizational characteristics will be explored as potential moderators and mediators of implementation outcomes. Implementation costs for each of the training methods will also be assessed. The present study involves partnering with college counseling centers to determine the most effective method to implement IPT for depression and eating disorders in these settings. The results of this study will inform future large-scale dissemination of clinical interventions to mental health service providers by providing evidence for the selection of training methods when an agency chooses to adopt new interventions.

Comparison of 2 family therapies for adolescent anorexia nervosa: a randomized parallel trial.

IMPORTANCE: Anorexia nervosa (AN) is a serious disorder with high rates of morbidity and mortality. Family-based treatment (FBT) is an evidence-based therapy for adolescent AN, but less than half of those who receive this approach recover. Hence, it is important to identify other approaches to prevent the development of the chronic form of AN for which there is no known evidence-based treatment. OBJECTIVE: To compare FBT with systemic family therapy (SyFT) for the treatment of adolescent-onset AN. DESIGN, SETTING, AND PARTICIPANTS: Research in Anorexia Nervosa (RIAN) is a 2-group (FBT and SyFT) randomized trial conducted between September 2005 and April 2012. Interviewers were blinded to the treatment condition. A total of 564 adolescents receiving care at 6 outpatient clinics experienced in the treatment of AN were screened. Of these, 262 adolescents did not meet the inclusion criteria and 138 declined to participate; hence, 164 adolescents (aged 12-18 years) of both sexes meeting the criteria for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, AN (except for amenorrhea) were enrolled. Three participants were withdrawn from FBT and 7 were withdrawn from SyFT after serious adverse events occurred. INTERVENTIONS: Two manualized family therapies with 16 one-hour sessions during 9 months. Family-based therapy focuses on the facilitation of weight gain, whereas SyFT addresses general family processes. MAIN OUTCOMES AND MEASURES: The primary outcomes were percentage of ideal body weight (IBW) and remission (≥95% of IBW). The a priori hypothesis was that FBT would result in faster weight gain early in treatment and at the end of treatment (EOT). RESULTS: There were no statistically significant differences between treatment groups for the primary outcome, for eating disorder symptoms or comorbid psychiatric disorders at the EOT or follow-up. Remission rates included FBT, 33.1% at the EOT and 40.7% at follow-up and SyFT, 25.3% and 39.0%, respectively. Family-based therapy led to significantly faster weight gain early in treatment, significantly fewer days in the hospital, and lower treatment costs per patient in remission at the EOT (FBT, $8963; SyFT, $18 005). An exploratory moderator analysis found that SyFT led to greater weight gain than did FBT for participants with more severe obsessive-compulsive symptoms. CONCLUSIONS AND RELEVANCE: The findings of this study suggest that FBT is the preferred treatment for adolescent AN because it is not significantly different from SyFT and leads to similar outcomes at a lower cost than SyFT. Adolescents with more severe obsessive-compulsive symptoms may receive more benefits with SyFT. TRIAL REGISTRATION: clinicaltrials.gov Identifier NCT00610753.

On the use of propensity scores in principal causal effect estimation.

We examine the practicality of propensity score methods for estimating causal treatment effects conditional on intermediate posttreatment outcomes (principal effects) in the context of randomized experiments. In particular, we focus on the sensitivity of principal causal effect estimates to violation of principal ignorability, which is the primary assumption that underlies the use of propensity score methods to estimate principal effects. Under principal ignorability (PI), principal strata membership is conditionally independent of the potential outcome under control given the pre-treatment covariates; i.e. there are no differences in the potential outcomes under control across principal strata given the observed pretreatment covariates. Under this assumption, principal scores modeling principal strata membership can be estimated based solely on the observed covariates and used to predict strata membership and estimate principal effects. While this assumption underlies the use of propensity scores in this setting, sensitivity to violations of it has not been studied rigorously. In this paper, we explicitly define PI using the outcome model (although we do not actually use this outcome model in estimating principal scores) and systematically examine how deviations from the assumption affect estimates, including how the strength of association between principal stratum membership and covariates modifies the performance. We find that when PI is violated, very strong covariate predictors of stratum membership are needed to yield accurate estimates of principal effects.

Adaptive designs for randomized trials in public health.

In this article, we present a discussion of two general ways in which the traditional randomized trial can be modified or adapted in response to the data being collected. We use the term adaptive design to refer to a trial in which characteristics of the study itself, such as the proportion assigned to active intervention versus control, change during the trial in response to data being collected. The term adaptive sequence of trials refers to a decision-making process that fundamentally informs the conceptualization and conduct of each new trial with the results of previous trials. Our discussion below investigates the utility of these two types of adaptations for public health evaluations. Examples are provided to illustrate how adaptation can be used in practice. From these case studies, we discuss whether such evaluations can or should be analyzed as if they were formal randomized trials, and we discuss practical as well as ethical issues arising in the conduct of these new-generation trials.

Causal inference in randomized experiments with mediational processes.

This article links the structural equation modeling (SEM) approach with the principal stratification (PS) approach, both of which have been widely used to study the role of intermediate posttreatment outcomes in randomized experiments. Despite the potential benefit of such integration, the 2 approaches have been developed in parallel with little interaction. This article proposes the cross-model translation (CMT) approach, in which parameter estimates are translated back and forth between the PS and SEM models. First, without involving any particular identifying assumptions, translation between PS and SEM parameters is carried out on the basis of their close conceptual connection. Monte Carlo simulations are used to further clarify the relation between the 2 approaches under particular identifying assumptions. The study concludes that, under the common goal of causal inference, what makes a practical difference is the choice of identifying assumptions, not the modeling framework itself. The CMT approach provides a common ground in which the PS and SEM approaches can be jointly considered, focusing on their common inferential problems.

Intention-to-treat analysis in cluster randomized trials with noncompliance.

In cluster randomized trials (CRTs), individuals belonging to the same cluster are very likely to resemble one another, not only in terms of outcomes but also in terms of treatment compliance behavior. Although the impact of resemblance in outcomes is well acknowledged, little attention has been given to the possible impact of resemblance in compliance behavior. This study defines compliance intraclass correlation as the level of resemblance in compliance behavior among individuals within clusters. On the basis of Monte Carlo simulations, it is demonstrated how compliance intraclass correlation affects power to detect intention-to-treat (ITT) effect in the CRT setting. As a way of improving power to detect ITT effect in CRTs accompanied by noncompliance, this study employs an estimation method, where ITT effect estimates are obtained based on compliance-type-specific treatment effect estimates. A multilevel mixture analysis using an ML-EM estimation method is used for this estimation.

Cluster randomized trials with treatment noncompliance.

Cluster randomized trials (CRTs) have been widely used in field experiments treating a cluster of individuals as the unit of randomization. This study focused particularly on situations where CRTs are accompanied by a common complication, namely, treatment noncompliance or, more generally, intervention nonadherence. In CRTs, compliance may be related not only to individual characteristics but also to the environment of clusters individuals belong to. Therefore, analyses ignoring the connection between compliance and clustering may not provide valid results. Although randomized field experiments often suffer from both noncompliance and clustering of the data, these features have been studied as separate rather than concurrent problems. On the basis of Monte Carlo simulations, this study demonstrated how clustering and noncompliance may affect statistical inferences and how these two complications can be accounted for simultaneously. In particular, the effect of the intervention on individuals who not only were assigned to active intervention but also abided by this intervention assignment (complier average causal effect) was the focus. For estimation of intervention effects considering noncompliance and data clustering, an ML-EM estimation method was employed.