Literature DB >> 28110111

Applying a novel statistical method to advance the personalized treatment of anxiety disorders: A composite moderator of comparative drop-out from CBT and ACT.

Andrea N Niles1, Kate B Wolitzky-Taylor2, Joanna J Arch3, Michelle G Craske4.   

Abstract

BACKGROUND: No prior studies have examined moderators of dropout between distinct treatments for anxiety disorders. This study applied a novel statistical approach for examining moderators of dropout from traditional cognitive behavioral therapy (CBT) and acceptance and commitment therapy (ACT).
METHOD: We combined data from two randomized controlled trials (N = 208) comparing CBT and ACT for patients with DSM-IV anxiety disorders. Adapting Kraemer's method for constructing and evaluating composite moderators (2013), 26 variables were examined for individual effect sizes. Forward-stepwise regression combined with k-fold cross validation was used to identify a model to predict treatment dropout.
RESULTS: Four baseline variables comprised the final composite moderator: self-reported degree of control over internal anxiety, current psychiatric medication use, religiosity, and endurance in a voluntary hyperventilation stressor. This composite moderator predicted differential dropout from ACT vs. CBT with a medium effect size (r = 0.28), and had a significantly larger effect size than any individual moderator.
CONCLUSIONS: Findings reveal that specific patient profiles predict differential dropout from ACT vs. CBT for anxiety disorders. In the first investigation of a composite moderator with a dichotomous outcome, findings also support the superiority of composite over individual moderators.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Anxiety disorders; Attrition; Behavioral therapy; Moderators; Personalized medicine

Mesh:

Year:  2017        PMID: 28110111     DOI: 10.1016/j.brat.2017.01.001

Source DB:  PubMed          Journal:  Behav Res Ther        ISSN: 0005-7967


  6 in total

1.  Getting to precision psychopharmacology: Combining clinical and genetic information to predict fat gain from aripiprazole.

Authors:  H Oughli; E J Lenze; A E Locke; M D Yingling; Y Zhong; J P Miller; C F Reynolds; B H Mulsant; J W Newcomer; T R Peterson; D J Müller; G E Nicol
Journal:  J Psychiatr Res       Date:  2019-04-23       Impact factor: 4.791

2.  Advancing Personalized Medicine: Application of a Novel Statistical Method to Identify Treatment Moderators in the Coordinated Anxiety Learning and Management Study.

Authors:  Andrea N Niles; Amanda G Loerinc; Jennifer L Krull; Peter Roy-Byrne; Greer Sullivan; Cathy D Sherbourne; Alexander Bystritsky; Michelle G Craske
Journal:  Behav Ther       Date:  2017-02-23

Review 3.  [Anxiety disorders: which psychotherapy for whom?]

Authors:  A Ströhle; T Fydrich
Journal:  Nervenarzt       Date:  2018-03       Impact factor: 1.214

4.  Promise and Challenges of Using Combined Moderator Methods to Personalize Mental Health Treatment.

Authors:  Meredith L Wallace; Stephen F Smagula
Journal:  Am J Geriatr Psychiatry       Date:  2018-04-24       Impact factor: 4.105

5.  Precision medicine for long-term depression outcomes using the Personalized Advantage Index approach: cognitive therapy or interpersonal psychotherapy?

Authors:  Suzanne C van Bronswijk; Robert J DeRubeis; Lotte H J M Lemmens; Frenk P M L Peeters; John R Keefe; Zachary D Cohen; Marcus J H Huibers
Journal:  Psychol Med       Date:  2019-11-22       Impact factor: 7.723

6.  Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.

Authors:  Gonzalo Salazar de Pablo; Erich Studerus; Julio Vaquerizo-Serrano; Jessica Irving; Ana Catalan; Dominic Oliver; Helen Baldwin; Andrea Danese; Seena Fazel; Ewout W Steyerberg; Daniel Stahl; Paolo Fusar-Poli
Journal:  Schizophr Bull       Date:  2021-03-16       Impact factor: 9.306

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.