Category: Adult Anxiety

Symposium

Symposium 83 - Data-Driven Approaches to Exploring Heterogeneity in Response to Treatments for Mood and Anxiety Disorders

Saturday, November 18
12:00 PM - 1:30 PM
Location: Indigo Ballroom E, Level 2, Indigo Level

Keywords: Anxiety | Randomized Controlled Trial | Research Methods
Presentation Type: Symposium

Cognitive-behavioral-based intervention and prevention approaches to treat anxiety and related conditions have demonstrated effective treatment effects on average. However, focusing solely on these average treatment effects masks large between-individual variance in treatment effects and may result in researchers and clinicians mistakenly believing in the effectiveness of a treatment for all when in fact the treatment only works for a minority of people. This approach is also contrary to recent patient-centered approaches to treatment, which recognize that not all treatments work effectively for all people. Identifying for whom treatments work and what factors account for treatment success should allow clinical researchers to develop more effective patient-centered interventions. 


Person-centered data-driven approaches offer a novel approach to identify for whom treatments are successful as well as to determine baseline characteristics associated with treatment success. These approaches involve identifying unobserved classes of people based on common latent characteristics. Within this symposium, we will discuss the use of various data-analytic approaches to exploring heterogeneity in treatment response. A study will be presented focusing on the use of growth mixture modeling to classify Veterans receiving prolonged exposure for PTSD into treatment responders and non-responders. Baseline predictors of treatment response classes will also be considered. Another study will also detail the use of growth mixture modeling, within in a preventative intervention targeting anxiety in a sample of adult daily smokers. This study will also consider potential predictors of treatment responding. A third study will focus on the use of latent class growth analysis to explore patterns of treatment drop-out in a randomized clinical trial comparing Concurrent Treatment of PTSD and Substance Use Disorders Using Prolonged Exposure (COPE) and Relapse Prevention Therapy (RPT) in a sample of people with comorbid PTSD (and subthreshold PTSD) and substance use disorder diagnoses. This study will then compare reductions in PTSD and substance use across these classes. The final set of studies will focus on validating prior factor mixture modeling-derived anxiety sensitivity classes by exploring treatment responding by class in two different samples receiving a brief anxiety sensitivity intervention versus a physical health education control condition. Together, these studies will highlight the many roles that data-driven approaches can play in uncovering treatment response at a more person-centered level. 

Learning Objectives:

Nicholas P. Allan

Assistant Professor
Ohio University

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Michael J. Zvolensky

Hugh Roy and Lillie Cranz Cullen Distinguished University Professor
University of Houston and The University of Texas MD Anderson Cancer Center

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    Nicholas P. Allan

    Assistant Professor
    Ohio University

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    Brad B. Schmidt

    FSU

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    Amanda Medley. Raines

    Post-Doctoral Fellow
    Southeast Louisiana Veterans HealthCare System

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    Santiago Papini

    Graduate Student
    University of Texas

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