CBT for Childhood Anxiety: Reviewing the State of Personalised Intervention Research

This article presents a mini-review of the state of personalised intervention research in the field of child and adolescent anxiety. We evaluated narrative, systematic and meta-analytic reviews of key research methodologies and how they relate to current approaches for personalising CBT, specifically. Preliminary evidence of predictors (severity of primary disorder, social anxiety disorder (SoAD), comorbid depression, parental psychopathology, parental involvement and duration of treatment), moderators (type of primary disorder) and mediators (self-talk, coping, problem-solving and comorbid symptoms) of CBT outcomes provides content for several personalised approaches to treatment. Finally, we present a novel conceptual model depicting the state of personalised intervention research in childhood anxiety and propose a research agenda for continued progress.

Identifying treatment moderators of a trauma-informed parenting intervention with children in foster care: Using model-based recursive partitioning

Background : Trauma-informed parenting interventions have been used in child welfare to help caregivers respond to children in trauma-informed ways that can mitigate the effects of maltreatment and build strong caregiver-child relationships. Existing studies support its effectiveness with the foster care population. However, to further advance its development, one key step is to identify subgroups of participants that respond differently from the intervention. Objective: To identify pre-treatment moderators that can distinguish subgroups of caregivers and children that benefit differently from the intervention. Participants and setting: 414 foster care children (age 3 or younger) and their caregivers were assigned either to the trauma-informed parenting intervention in the Illinois Birth through Three Title IV-E waiver demonstration or to a comparison group that received services as usual. Methods: Model-based Recursive Partitioning (MOB) was used to identify treatment moderators and moderator interactions. MOB fits a parametric model and uses a data-driven method to find subgroups for which the specified parametric model has different parameters. Two parametric models (logistic and linear regression) were used in accordance with two outcomes: reunification (binary), and caregiver-child attachment (continuous). We examined 21 potential pre-treatment moderators in both models. Results: For reunification outcome, three treatment moderators interact to produce different subgroups of participants who responded differently to the intervention: (a) caregivers’ relationship status (kin vs. non-kin/permanent caregivers), (b) caregiver-child attachment, and (c) case history of physical abuse. For attachment outcome, caregivers’ age was found to be a treatment moderator. Future developments of trauma-informed interventions should consider these moderators.

Identifying back pain subgroups: developing and applying approaches using individual patient data collected within clinical trials

BackgroundThere is good evidence that therapist-delivered interventions have modest beneficial effects for people with low back pain (LBP). Identification of subgroups of people with LBP who may benefit from these different treatment approaches is an important research priority.Aim and objectivesTo improve the clinical effectiveness and cost-effectiveness of LBP treatment by providing patients, their clinical advisors and health-service purchasers with better information about which participants are most likely to benefit from which treatment choices. Our objectives were to synthesise what is already known about the validity, reliability and predictive value of possible treatment moderators (patient factors that predict response to treatment) for therapist-delivered interventions; develop a repository of individual participant data from randomised controlled trials (RCTs) testing therapist-delivered interventions for LBP; determine which participant characteristics, if any, predict clinical response to different treatments for LBP; and determine which participant characteristics, if any, predict the most cost-effective treatments for LBP. Achieving these objectives required substantial methodological work, including the development and evaluation of some novel statistical approaches. This programme of work was not designed to analyse the main effect of interventions and no such interpretations should be made.MethodsFirst, we reviewed the literature on treatment moderators and subgroups. We initially invited investigators of trials of therapist-delivered interventions for LBP with > 179 participants to share their data with us; some further smaller trials that were offered to us were also included. Using these trials we developed a repository of individual participant data of therapist-delivered interventions for LBP. Using this data set we sought to identify which participant characteristics, if any, predict response to different treatments (moderators) for clinical effectiveness and cost-effectiveness outcomes. We undertook an analysis of covariance to identify potential moderators to apply in our main analyses. Subsequently, we developed and applied three methods of subgroup identification: recursive partitioning (interaction trees and subgroup identification based on a differential effect search); adaptive risk group refinement; and an individual participant data indirect network meta-analysis (NWMA) to identify subgroups defined by multiple parameters.ResultsWe included data from 19 RCTs with 9328 participants (mean age 49 years, 57% females). Our prespecified analyses using recursive partitioning and adaptive risk group refinement performed well and allowed us to identify some subgroups. The differences in the effect size in the different subgroups were typically small and unlikely to be clinically meaningful. Increasing baseline severity on the outcome of interest was the strongest driver of subgroup identification that we identified. Additionally, we explored the application of Bayesian indirect NWMA. This method produced varying probabilities that a particular treatment choice would be most likely to be effective for a specific patient profile.ConclusionsThese data lack clinical effectiveness or cost-effectiveness justification for the use of baseline characteristics in the development of subgroups for back pain. The methodological developments from this work have the potential to be applied in other clinical areas. The pooled repository database will serve as a valuable resource to the LBP research community.FundingThe National Institute for Health Research Programme Grants for Applied Research programme. This project benefited from facilities funded through Birmingham Science City Translational Medicine Clinical Research and Infrastructure Trials Platform, with support from Advantage West Midlands (AWM) and the Wolfson Foundation.