Faculty Bibliography

Recent changes to US research funding are having far-reaching consequences that imperil the integrity of science and the provision of care to vulnerable populations. Resisting these changes, the BJPsych Portfolio reaffirms its commitment to publishing mental science and advancing psychiatric knowledge that improves the mental health of one and all.

OBJECTIVE:Naturalistic developmental behavioral interventions for children with autism spectrum disorder show evidence for effectiveness for specific social communication targets such as joint attention or engagement. However, combining evidence from different studies and comparing intervention effects across those studies have not been feasible due to lack of a standardized outcome measure of broader social communication skills that can be applied uniformly across trials. This investigation examined the usefulness of the Brief Observation of Social Communication Change (BOSCC) as a common outcome measure of general social communication skills based on secondary analyses of data obtained from previously conducted randomized controlled trials of 3 intervention models, Early Social Intervention (ESI), Early Start Denver Model (ESDM) and Joint Attention Symbolic Play Engagement and Regulation (JASPER). METHOD/METHODS:The subset of datasets from the 3 randomized controlled trials was created to examine differences in the BOSCC scores between intervention and control groups over the course of the interventions. RESULTS:Based on 582 videos from 207 caregiver-child dyads, the BOSCC noted significant differences between intervention vs control groups in broad social communication skills within 2 of the 3 intervention models, which were longer in duration and focused on a broad range of developmental skills. CONCLUSION/CONCLUSIONS:The BOSCC offers the potential to take a uniform measurement approach across different intervention models to capture the effect of intervention on general social communication skills but may not pick up the effects of some brief interventions targeting proximal outcomes. CLINICAL TRIAL REGISTRATION INFORMATION/BACKGROUND:Comparing Parent-Implemented Interventions for Toddlers With Autism Spectrum Disorders; https://www. CLINICALTRIALS/RESULTS:gov/; NCT00760812. Intensive Intervention for Toddlers With Autism (EARLY STEPS); https://www. CLINICALTRIALS/RESULTS:gov/; NCT00698997. Social and Communication Outcomes for Young Children With Autism; https://www. CLINICALTRIALS/RESULTS:gov/; NCT00953095.

OBJECTIVE:This individual participant data meta-analysis aimed to investigate the effectiveness of cognitive behavioral therapy with a trauma focus (CBT-TF) for posttraumatic stress disorder (PTSD). Furthermore, we examined the effect of moderators on PTSD symptom severity. METHOD/METHODS:This study included randomized controlled trials comparing CBT-TF to an inactive or active comparison group for adults with PTSD. The primary and secondary outcomes were PTSD symptom severity and remission, respectively. Moderators included sociodemographic and clinical variables. RESULTS:Twelve studies compared CBT-TF with inactive (n = 625) and 11 with active comparison conditions (n = 706). The one-stage individual participant data meta-analysis found that CBT-TF was more effective than inactive comparison conditions (β = -0.78; OR = 2.34) and not significantly different from active comparison conditions (β = 0.02; OR = 0.53) in reducing PTSD symptom severity and achieving PTSD remission, respectively. When comparing CBT-TF with inactive treatments, moderator analysis found that divorced participants had greater PTSD symptoms postintervention following CBT-TF than participants who were single, cohabitating, or married receiving CBT-TF, both in the completer (β = 0.93) and full-sample (β = 0.59) analyses. For the active treatment comparison, moderator analysis found that participants taking psychotropic medication had lower PTSD symptoms following CBT-TF than those not taking psychotropic medication in the completer analysis (β = -0.39). CONCLUSION/CONCLUSIONS:Based on our moderator analyses, further research is needed to understand the effect of psychotropic medication on the CBT-TF intervention process. Moreover, divorced participants with PTSD receiving CBT-TF might benefit from enhanced support. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

BACKGROUND:Available empirical evidence on participant-level factors associated with dropout from psychotherapies for post-traumatic stress disorder (PTSD) is both limited and inconclusive. More comprehensive understanding of the various factors that contribute to study dropout from cognitive-behavioural therapy with a trauma focus (CBT-TF) is crucial for enhancing treatment outcomes. OBJECTIVE:Using an individual participant data meta-analysis (IPD-MA) design, we examined participant-level predictors of study dropout from CBT-TF interventions for PTSD. METHODS:A comprehensive systematic literature search was undertaken to identify randomised controlled trials comparing CBT-TF with waitlist control, treatment-as-usual or another therapy. Academic databases were screened from conception until 11 January 2021. Eligible interventions were required to be individual and in-person delivered. Participants were considered dropouts if they did not complete the post-treatment assessment. FINDINGS/RESULTS:The systematic literature search identified 81 eligible studies (n=3330). Data were pooled from 25 available CBT-TF studies comprising 823 participants. Overall, 221 (27%) of the 823 dropped out. Of 581 civilians, 133 (23%) dropped out, as did 75 (42%) of 178 military personnel/veterans. Bivariate and multivariate analyses indicated that military personnel/veterans (RR 2.37) had a significantly greater risk of dropout than civilians. Furthermore, the chance of dropping out significantly decreased with advancing age (continuous; RR 0.98). CONCLUSIONS:These findings underscore the risk of premature termination from CBT-TF among younger adults and military veterans/personnel. CLINICAL IMPLICATION/CONCLUSIONS:Understanding predictors can inform the development of retention strategies tailored to at-risk subgroups, enhance engagement, improve adherence and yield better treatment outcomes.

BACKGROUND:Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. METHODS:To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. RESULTS:We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analyses demonstrate the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. CONCLUSION/CONCLUSIONS:The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

BACKGROUND:There is a need to understand the relationship between COVID-19 Convalescent Plasma (CCP) anti-SARS-CoV-2 IgG levels and clinical outcomes to optimize CCP use. This study aims to evaluate the relationship between recipient baseline clinical status, clinical outcomes, and CCP antibody levels. METHODS:The study analyzed data from the COMPILE study, a meta-analysis of pooled individual patient data from 8 randomized clinical trials (RCTs) assessing the efficacy of CCP vs. control, in adults hospitalized for COVID-19 who were not receiving mechanical ventilation at randomization. SARS-CoV-2 IgG levels, referred to as 'dose' of CCP treatment, were retrospectively measured in donor sera or the administered CCP, semi-quantitatively using the VITROS Anti-SARS-CoV-2 IgG chemiluminescent immunoassay (Ortho-Clinical Diagnostics) with a signal-to-cutoff ratio (S/Co). The association between CCP dose and outcomes was investigated, treating dose as either continuous or categorized (higher vs. lower vs. control), stratified by recipient oxygen supplementation status at presentation. RESULTS:A total of 1714 participants were included in the study, 1138 control- and 576 CCP-treated patients for whom donor CCP anti-SARS-CoV2 antibody levels were available from the COMPILE study. For participants not receiving oxygen supplementation at baseline, higher-dose CCP (/control) was associated with a reduced risk of ventilation or death at day 14 (OR = 0.19, 95% CrI: [0.02, 1.70], posterior probability Pr(OR < 1) = 0.93) and day 28 mortality (OR = 0.27 [0.02, 2.53], Pr(OR < 1) = 0.87), compared to lower-dose CCP (/control) (ventilation or death at day 14 OR = 0.79 [0.07, 6.87], Pr(OR < 1) = 0.58; and day 28 mortality OR = 1.11 [0.10, 10.49], Pr(OR < 1) = 0.46), exhibiting a consistently positive CCP dose effect on clinical outcomes. For participants receiving oxygen at baseline, the dose-outcome relationship was less clear, although a potential benefit for day 28 mortality was observed with higher-dose CCP (/control) (OR = 0.66 [0.36, 1.13], Pr(OR < 1) = 0.93) compared to lower-dose CCP (/control) (OR = 1.14 [0.73, 1.78], Pr(OR < 1) = 0.28). CONCLUSION/CONCLUSIONS:Higher-dose CCP is associated with its effectiveness in patients not initially receiving oxygen supplementation, however, further research is needed to understand the interplay between CCP anti-SARS-CoV-2 IgG levels and clinical outcome in COVID-19 patients initially receiving oxygen supplementation.

We consider unsupervised classification by means of a latent multinomial variable which categorizes a scalar response into one of the L components of a mixture model which incorporates scalar and functional covariates. This process can be thought as a hierarchical model with the first level modelling a scalar response according to a mixture of parametric distributions and the second level modelling the mixture probabilities by means of a generalized linear model with functional and scalar covariates. The traditional approach of treating functional covariates as vectors not only suffers from the curse of dimensionality, since functional covariates can be measured at very small intervals leading to a highly parametrized model, but also does not take into account the nature of the data. We use basis expansions to reduce the dimensionality and a Bayesian approach for estimating the parameters while providing predictions of the latent classification vector. The method is motivated by two data examples that are not easily handled by existing methods. The first example concerns identifying placebo responders on a clinical trial (normal mixture model) and the other predicting illness for milking cows (zero-inflated mixture of the Poisson model).

BACKGROUND/UNASSIGNED:Precision medicine has led to the development of targeted treatment strategies tailored to individual patients based on their characteristics and disease manifestations. Although precision medicine often focuses on a single health outcome for individualized treatment decision rules (ITRs), relying only on a single outcome rather than all available outcomes information leads to suboptimal data usage when developing optimal ITRs. METHODS/UNASSIGNED:To address this limitation, we propose a Bayesian multivariate hierarchical model that leverages the wealth of correlated health outcomes collected in clinical trials. The approach jointly models mixed types of correlated outcomes, facilitating the "borrowing of information" across the multivariate outcomes, and results in a more accurate estimation of heterogeneous treatment effects compared to using single regression models for each outcome. We develop a treatment benefit index, which quantifies the relative treatment benefit of the experimental treatment over the control treatment, based on the proposed multivariate outcome model. RESULTS/UNASSIGNED:We demonstrate the strengths of the proposed approach through extensive simulations and an application to an international Coronavirus Disease 2019 (COVID-19) treatment trial. Simulation results indicate that the proposed method reduces the occurrence of erroneous treatment decisions compared to a single regression model for a single health outcome. Additionally, the sensitivity analysis demonstrates the robustness of the model across various study scenarios. Application of the method to the COVID-19 trial exhibits improvements in estimating the individual-level treatment efficacy (indicated by narrower credible intervals for odds ratios) and optimal ITRs. CONCLUSION/UNASSIGNED:The study jointly models mixed types of outcomes in the context of developing ITRs. By considering multiple health outcomes, the proposed approach can advance the development of more effective and reliable personalized treatment.

The non-reporting of negative studies results in a scientific record that is incomplete, one-sided and misleading. The consequences of this range from inappropriate initiation of further studies that might put participants at unnecessary risk to treatment guidelines that may be in error, thus compromising day-to-day clinical practice.