Faculty Bibliography

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.

A novel functional additive model is proposed, which is uniquely modified and constrained to model nonlinear interactions between a treatment indicator and a potentially large number of functional and/or scalar pretreatment covariates. The primary motivation for this approach is to optimize individualized treatment rules based on data from a randomized clinical trial. We generalize functional additive regression models by incorporating treatment-specific components into additive effect components. A structural constraint is imposed on the treatment-specific components in order to provide a class of additive models with main effects and interaction effects that are orthogonal to each other. If primary interest is in the interaction between treatment and the covariates, as is generally the case when optimizing individualized treatment rules, we can thereby circumvent the need to estimate the main effects of the covariates, obviating the need to specify their form and thus avoiding the issue of model misspecification. The methods are illustrated with data from a depression clinical trial with electroencephalogram functional data as patients' pretreatment covariates.

BACKGROUND:Numerous clinical trials have been initiated to find effective treatments for COVID-19. These trials have often been initiated in regions where the pandemic has already peaked. Consequently, achieving full enrollment in a single trial might require additional COVID-19 surges in the same location over several years. This has inspired us to pool individual patient data (IPD) from ongoing, paused, prematurely-terminated, or completed randomized controlled trials (RCTs) in real-time, to find an effective treatment as quickly as possible in light of the pandemic crisis. However, pooling across trials introduces enormous uncertainties in study design (e.g., the number of RCTs and sample sizes might be unknown in advance). We sought to develop a versatile treatment efficacy assessment model that accounts for these uncertainties while allowing for continuous monitoring throughout the study using Bayesian monitoring techniques. METHODS:We provide a detailed look at the challenges and solutions for model development, describing the process that used extensive simulations to enable us to finalize the analysis plan. This includes establishing prior distribution assumptions, assessing and improving model convergence under different study composition scenarios, and assessing whether we can extend the model to accommodate multi-site RCTs and evaluate heterogeneous treatment effects. In addition, we recognized that we would need to assess our model for goodness-of-fit, so we explored an approach that used posterior predictive checking. Lastly, given the urgency of the research in the context of evolving pandemic, we were committed to frequent monitoring of the data to assess efficacy, and we set Bayesian monitoring rules calibrated for type 1 error rate and power. RESULTS:The primary outcome is an 11-point ordinal scale. We present the operating characteristics of the proposed cumulative proportional odds model for estimating treatment effectiveness. The model can estimate the treatment's effect under enormous uncertainties in study design. We investigate to what degree the proportional odds assumption has to be violated to render the model inaccurate. We demonstrate the flexibility of a Bayesian monitoring approach by performing frequent interim analyses without increasing the probability of erroneous conclusions. CONCLUSION:This paper describes a translatable framework using simulation to support the design of prospective IPD meta-analyses.

OBJECTIVES/OBJECTIVE:Disengagement from treatment is common in first episode schizophrenia (FES) and is associated with poor outcomes. Our aim was to determine whether hippocampal subfield volumes predict disengagement during maintenance treatment of FES. METHODS:FES patients were recruited from sites in Boston, New York, Shanghai, and Changsha. After stabilization on antipsychotic medication, participants were randomized to add-on citalopram or placebo and followed for 12 months. Demographic, clinical and cognitive factors at baseline were compared between completers and disengagers in addition to volumes of hippocampal subfields. RESULTS:Baseline data were available for 95 randomized participants. Disengagers (n = 38, 40%) differed from completers (n = 57, 60%) by race (more likely Black; less likely Asian) and in more alcohol use, parkinsonism, negative symptoms and more impairment in visual learning and working memory. Bilateral dentate gyrus (DG), CA1, CA2/3 and whole hippocampal volumes were significantly smaller in disengagers compared to completers. When all the eight volumes were entered into the model simultaneously, only left DG volume significantly predicted disengagement status and remained significant after adjusting for age, sex, race, intracranial volume, antipsychotic dose, duration of untreated psychosis, citalopram status, alcohol status, and smoking status (P < .01). Left DG volume predicted disengagement with 57% sensitivity and 83% specificity. CONCLUSIONS:Smaller left DG was significantly associated with disengagement status over 12 months of maintenance treatment in patients with FES participating in a randomized clinical trial. If replicated, these findings may provide a biomarker to identify patients at risk for disengagement and a potential target for interventions.

This paper develops a Bayesian model with a flexible link function connecting a binary treatment response to a linear combination of covariates and a treatment indicator and the interaction between the two. Generalized linear models allowing data-driven link functions are often called "single-index models" and are among popular semi-parametric modeling methods. In this paper, we focus on modeling heterogeneous treatment effects, with the goal of developing a treatment benefit index (TBI) incorporating prior information from historical data. The model makes inference on a composite moderator of treatment effects, summarizing the effect of the predictors within a single variable through a linear projection of the predictors. This treatment benefit index can be useful for stratifying patients according to their predicted treatment benefit levels and can be especially useful for precision health applications. The proposed method is applied to a COVID-19 treatment study.

Evolving medical technologies have motivated the development of treatment decision rules (TDRs) that incorporate complex, costly data (e.g., imaging). In clinical practice, we aim for TDRs to be valuable by reducing unnecessary testing while still identifying the best possible treatment for a patient. Regardless of how well any TDR performs in the target population, there is an associated degree of uncertainty about its optimality for a specific patient. In this paper, we aim to quantify, via a confidence measure, the uncertainty in a TDR as patient data from sequential procedures accumulate in real-time. We first propose estimating confidence using the distance of a patient's vector of covariates to a treatment decision boundary, with further distances corresponding to higher certainty. We further propose measuring confidence through the conditional probabilities of ultimately (with all possible information available) being assigned a particular treatment, given that the same treatment is assigned with the patient's currently available data or given the treatment recommendation made using only the currently available patient data. As patient data accumulate, the treatment decision is updated and confidence reassessed until a sufficiently high confidence level is achieved. We present results from simulation studies and illustrate the methods using a motivating example from a depression clinical trial. Recommendations for practical use of the measures are proposed.

OBJECTIVES/OBJECTIVE:We designed this study to test whether clazakizumab, a direct interleukin-6 inhibitor, benefits patients hospitalized with severe or critical COVID-19 disease accompanied by hyperinflammation. DESIGN/METHODS:Multicenter, randomized, double-blinded, placebo-controlled, seamless phase II/III trial. SETTING/METHODS:Five U.S. medical centers. PATIENTS/METHODS:Adults inpatients with severe COVID-19 disease and hyperinflammation. INTERVENTIONS/METHODS:Eighty-one patients enrolled in phase II, randomized 1:1:1 to low-dose (12.5 mg) or high-dose (25 mg) clazakizumab or placebo. Ninety-seven patients enrolled in phase III, randomized 1:1 to high-dose clazakizumab or placebo. MEASUREMENTS AND MAIN RESULTS/RESULTS:The primary outcome was 28-day ventilator-free survival. Secondary outcomes included overall survival ,frequency and duration of intubation, and frequency and duration of ICU admission. Per Data Safety and Monitoring Board recommendations, additional secondary outcomes describing clinical status and status changes, as measured by an ordinal scale, were added. Bayesian cumulative proportional odds, logistic, and Poisson regression models were used. The low-dose arm was dropped when the phase II study suggested superiority of the high-dose arm. We report on 152 patients, 74 randomized to placebo and 78 to high-dose clazakizumab. Patients receiving clazakizumab had greater odds of 28-day ventilator-free survival (odds ratio [OR] = 3.84; p [OR > 1] 99.9%), as well as overall survival at 28 and 60 days (OR = 1.75; p [OR > 1] 86.5% and OR = 2.53; p [OR > 1] 97.7%). Clazakizumab was associated with lower odds of intubation (OR = 0.2; p [OR] < 1; 99.9%) and ICU admission (OR = 0.26; p [OR < 1] 99.6%); shorter durations of ventilation and ICU stay (risk ratio [RR] < 0.75; p [RR < 1] > 99% for both); and greater odds of improved clinical status at 14, 28, and 60 days (OR = 2.32, p [OR > 1] 98.1%; OR = 3.36, p [OR > 1] 99.6%; and OR = 3.52, p [OR > 1] 99.8%, respectively). CONCLUSIONS:Clazakizumab significantly improved 28-day ventilator-free survival, 28- and 60-day overall survival, as well as clinical outcomes in hospitalized patients with COVID-19 and hyperinflammation.

SUMMARY/CONCLUSIONS:Poor research integrity is increasingly recognised as a serious problem in science. We outline some evidence for this claim and introduce the Royal College of Psychiatrists (RCPsych) journals' Research Integrity Group, which has been created to address this problem.

OBJECTIVE:The objective of this study was to systematically review existing empirical evidence on the effectiveness of trauma-specific treatment for justice-involved adolescents and evaluate the impact of the interventions on the reduction of posttraumatic stress disorder (PTSD) symptoms, co-occurring mental health symptoms, and juvenile justice-related outcomes. METHOD/METHODS:A systematic literature search was conducted using a four-step process. Studies were included if they used a manualized, trauma-specific treatment with at least one control or comparison group and a sample comprised exclusively of justice-involved adolescents. RESULTS:In total, 1,699 unique records were identified, and 56 full-text articles were reviewed, of which 7 met the criteria for inclusion. Trauma-specific interventions led to a decrease in PTSD symptoms compared with a control group in four of seven studies, and two studies also demonstrated a reduction in trauma-related depressive symptoms. Finally, juvenile justice-related outcomes were measured in only four studies, with one study finding moderately reduced rates of delinquent behavior and recidivism following trauma-specific treatment. CONCLUSIONS:The results from this systematic review suggest that trauma-specific treatment interventions have promising effects for justice-involved adolescents. However, the results reveal a dearth of quality intervention research for treating youths with histories of trauma in the justice system. Significant gaps in the literature are highlighted, and suggestions for future directions are discussed. (PsycInfo Database Record (c) 2021 APA, all rights reserved).