Positive thinking about negative studies.

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.

Association between COVID-19 convalescent plasma antibody levels and COVID-19 outcomes stratified by clinical status at presentation.

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: 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.

A sparse additive model for treatment effect-modifier selection.

Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This article develops a sparse additive model focused on estimation of treatment effect modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified. The proposed regression model can effectively identify treatment effect-modifiers that exhibit possibly nonlinear interactions with the treatment variable that are relevant for making optimal treatment decisions. A set of simulation experiments and an application to a dataset from a randomized clinical trial are presented to demonstrate the method.

Functional additive models for optimizing individualized treatment rules.

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.

Developing a Bayesian hierarchical model for a prospective individual patient data meta-analysis with continuous monitoring.

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.

A Randomized Double-Blinded Placebo Controlled Trial of Clazakizumab for the Treatment of COVID-19 Pneumonia With Hyperinflammation.

OBJECTIVES: 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: Multicenter, randomized, double-blinded, placebo-controlled, seamless phase II/III trial. SETTING: Five U.S. medical centers. PATIENTS: Adults inpatients with severe COVID-19 disease and hyperinflammation. INTERVENTIONS: 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: 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.

The integrity of the research record: a mess so big and so deep and so tall.

SUMMARY: 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.

A constrained single-index regression for estimating interactions between a treatment and covariates.

We consider a single-index regression model, uniquely constrained to estimate interactions between a set of pretreatment covariates and a treatment variable on their effects on a response variable, in the context of analyzing data from randomized clinical trials. We represent interaction effect terms of the model through a set of treatment-specific flexible link functions on a linear combination of the covariates (a single index), subject to the constraint that the expected value given the covariates equals 0, while leaving the main effects of the covariates unspecified. We show that the proposed semiparametric estimator is consistent for the interaction term of the model, and that the efficiency of the estimator can be improved with an augmentation procedure. The proposed single-index regression provides a flexible and interpretable modeling approach to optimizing individualized treatment rules based on patients' data measured at baseline, as illustrated by simulation examples and an application to data from a depression clinical trial.

Prospective individual patient data meta-analysis: Evaluating convalescent plasma for COVID-19.

As the world faced the devastation of the COVID-19 pandemic in late 2019 and early 2020, numerous clinical trials were initiated in many locations in an effort to establish the efficacy (or lack thereof) of potential treatments. As the pandemic has been shifting locations rapidly, individual studies have been at risk of failing to meet recruitment targets because of declining numbers of eligible patients with COVID-19 encountered at participating sites. It has become clear that it might take several more COVID-19 surges at the same location to achieve full enrollment and to find answers about what treatments are effective for this disease. This paper proposes an innovative approach for pooling patient-level data from multiple ongoing randomized clinical trials (RCTs) that have not been configured as a network of sites. We present the statistical analysis plan of a prospective individual patient data (IPD) meta-analysis (MA) from ongoing RCTs of convalescent plasma (CP). We employ an adaptive Bayesian approach for continuously monitoring the accumulating pooled data via posterior probabilities for safety, efficacy, and harm. Although we focus on RCTs for CP and address specific challenges related to CP treatment for COVID-19, the proposed framework is generally applicable to pooling data from RCTs for other therapies and disease settings in order to find answers in weeks or months, rather than years.

Long-term effects of maternal choline supplementation on CA1 pyramidal neuron gene expression in the Ts65Dn mouse model of Down syndrome and Alzheimer's disease.

Choline is critical for normative function of 3 major pathways in the brain, including acetylcholine biosynthesis, being a key mediator of epigenetic regulation, and serving as the primary substrate for the phosphatidylethanolamine N-methyltransferase pathway. Sufficient intake of dietary choline is critical for proper brain function and neurodevelopment. This is especially important for brain development during the perinatal period. Current dietary recommendations for choline intake were undertaken without critical evaluation of maternal choline levels. As such, recommended levels may be insufficient for both mother and fetus. Herein, we examined the impact of perinatal maternal choline supplementation (MCS) in a mouse model of Down syndrome and Alzheimer's disease, the Ts65Dn mouse relative to normal disomic littermates, to examine the effects on gene expression within adult offspring at ∼6 and 11 mo of age. We found MCS produces significant changes in offspring gene expression levels that supersede age-related and genotypic gene expression changes. Alterations due to MCS impact every gene ontology category queried, including GABAergic neurotransmission, the endosomal-lysosomal pathway and autophagy, and neurotrophins, highlighting the importance of proper choline intake during the perinatal period, especially when the fetus is known to have a neurodevelopmental disorder such as trisomy.-Alldred, M. J., Chao, H. M., Lee, S. H., Beilin, J., Powers, B. E., Petkova, E., Strupp, B. J., Ginsberg, S. D. Long-term effects of maternal choline supplementation on CA1 pyramidal neuron gene expression in the Ts65Dn mouse model of Down syndrome and Alzheimer's disease.

A Bayesian approach to joint modeling of matrix-valued imaging data and treatment outcome with applications to depression studies.

In this paper, we propose a unified Bayesian joint modeling framework for studying association between a binary treatment outcome and a baseline matrix-valued predictor. Specifically, a joint modeling approach relating an outcome to a matrix-valued predictor through a probabilistic formulation of multilinear principal component analysis is developed. This framework establishes a theoretical relationship between the outcome and the matrix-valued predictor, although the predictor is not explicitly expressed in the model. Simulation studies are provided showing that the proposed method is superior or competitive to other methods, such as a two-stage approach and a classical principal component regression in terms of both prediction accuracy and estimation of association; its advantage is most notable when the sample size is small and the dimensionality in the imaging covariate is large. Finally, our proposed joint modeling approach is shown to be a very promising tool in an application exploring the association between baseline electroencephalography data and a favorable response to treatment in a depression treatment study by achieving a substantial improvement in prediction accuracy in comparison to competing methods.

A single-index model with multiple-links.

In a regression model for treatment outcome in a randomized clinical trial, a treatment effect modifier is a covariate that has an interaction with the treatment variable, implying that the treatment efficacies vary across values of such a covariate. In this paper, we present a method for determining a composite variable from a set of baseline covariates, that can have a nonlinear association with the treatment outcome, and acts as a composite treatment effect modifier. We introduce a parsimonious generalization of the single-index models that targets the effect of the interaction between the treatment conditions and the vector of covariates on the outcome, a single-index model with multiple-links (SIMML) that estimates a single linear combination of the covariates (i.e., a single-index), with treatment-specific nonparametric link functions. The approach emphasizes a focus on the treatment-by-covariates interaction effects on the treatment outcome that are relevant for making optimal treatment decisions. Asymptotic results for estimator are obtained under possible model misspecification. A treatment decision rule based on the derived single-index is defined, and it is compared to other methods for estimating optimal treatment decision rules. An application to a clinical trial for the treatment of depression is presented.

CA1 pyramidal neuron gene expression mosaics in the Ts65Dn murine model of Down syndrome and Alzheimer's disease following maternal choline supplementation.

Although there are changes in gene expression and alterations in neuronal density and afferent inputs in the forebrain of trisomic mouse models of Down syndrome (DS) and Alzheimer's disease (AD), there is a lack of systematic assessments of gene expression and encoded proteins within individual vulnerable cell populations, precluding translational investigations at the molecular and cellular level. Further, no effective treatment exists to combat intellectual disability and basal forebrain cholinergic neurodegeneration seen in DS. To further our understanding of gene expression changes before and following cholinergic degeneration in a well-established mouse model of DS/AD, the Ts65Dn mouse, we assessed RNA expression levels from CA1 pyramidal neurons at two adult ages (∼6 months of age and ∼11 months of age) in both Ts65Dn and their normal disomic (2N) littermates. We further examined a therapeutic intervention, maternal choline supplementation (MCS), which has been previously shown to lessen dysfunction in spatial cognition and attention, and have protective effects on the survival of basal forebrain cholinergic neurons in the Ts65Dn mouse model. Results indicate that MCS normalized expression of several genes in key gene ontology categories, including synaptic plasticity, calcium signaling, and AD-associated neurodegeneration related to amyloid-beta peptide (Aß) clearance. Specifically, normalized expression levels were found for endothelin converting enzyme-2 (Ece2), insulin degrading enzyme (Ide), Dyrk1a, and calcium/calmodulin-dependent protein kinase II (Camk2a), among other relevant genes. Single population expression profiling of vulnerable CA1 pyramidal neurons indicates that MCS is a viable therapeutic for long-term reprogramming of key transcripts involved in neuronal signaling that are dysregulated in the trisomic mouse brain which have translational potential for DS and AD.

Development and evaluation of a multimodal marker of major depressive disorder.

This study aimed to identify biomarkers of major depressive disorder (MDD), by relating neuroimage-derived measures to binary (MDD/control), ordinal (severe MDD/mild MDD/control), or continuous (depression severity) outcomes. To address MDD heterogeneity, factors (severity of psychic depression, motivation, anxiety, psychosis, and sleep disturbance) were also used as outcomes. A multisite, multimodal imaging (diffusion MRI [dMRI] and structural MRI [sMRI]) cohort (52 controls and 147 MDD patients) and several modeling techniques-penalized logistic regression, random forest, and support vector machine (SVM)-were used. An additional cohort (25 controls and 83 MDD patients) was used for validation. The optimally performing classifier (SVM) had a 26.0% misclassification rate (binary), 52.2 ± 1.69% accuracy (ordinal) and r = .36 correlation coefficient (p < .001, continuous). Using SVM, R2 values for prediction of any MDD factors were <10%. Binary classification in the external data set resulted in 87.95% sensitivity and 32.00% specificity. Though observed classification rates are too low for clinical utility, four image-based features contributed to accuracy across all models and analyses-two dMRI-based measures (average fractional anisotropy in the right cuneus and left insula) and two sMRI-based measures (asymmetry in the volume of the pars triangularis and the cerebellum) and may serve as a priori regions for future analyses. The poor accuracy of classification and predictive results found here reflects current equivocal findings and sheds light on challenges of using these modalities for MDD biomarker identification. Further, this study suggests a paradigm (e.g., multiple classifier evaluation with external validation) for future studies to avoid nongeneralizable results.

Generated effect modifiers (GEM's) in randomized clinical trials.

In a randomized clinical trial (RCT), it is often of interest not only to estimate the effect of various treatments on the outcome, but also to determine whether any patient characteristic has a different relationship with the outcome, depending on treatment. In regression models for the outcome, if there is a non-zero interaction between treatment and a predictor, that predictor is called an "effect modifier". Identification of such effect modifiers is crucial as we move towards precision medicine, that is, optimizing individual treatment assignment based on patient measurements assessed when presenting for treatment. In most settings, there will be several baseline predictor variables that could potentially modify the treatment effects. This article proposes optimal methods of constructing a composite variable (defined as a linear combination of pre-treatment patient characteristics) in order to generate an effect modifier in an RCT setting. Several criteria are considered for generating effect modifiers and their performance is studied via simulations. An example from a RCT is provided for illustration.

Brain-Wide Insulin Resistance, Tau Phosphorylation Changes, and Hippocampal Neprilysin and Amyloid-ß Alterations in a Monkey Model of Type 1 Diabetes.

Epidemiological findings suggest that diabetic individuals are at a greater risk for developing Alzheimer's disease (AD). To examine the mechanisms by which diabetes mellitus (DM) may contribute to AD pathology in humans, we examined brain tissue from streptozotocin-treated type 1 diabetic adult male vervet monkeys receiving twice-daily exogenous insulin injections for 8-20 weeks. We found greater inhibitory phosphorylation of insulin receptor substrate 1 in each brain region examined of the diabetic monkeys when compared with controls, consistent with a pattern of brain insulin resistance that is similar to that reported in the human AD brain. Additionally, a widespread increase in phosphorylated tau was seen, including brain areas vulnerable in AD, as well as relatively spared structures, such as the cerebellum. An increase in active ERK1/2 was also detected, consistent with DM leading to changes in tau-kinase activity broadly within the brain. In contrast to these widespread changes, we found an increase in soluble amyloid-ß (Aß) levels that was restricted to the temporal lobe, with the greatest increase seen in the hippocampus. Consistent with this localized Aß increase, a hippocampus-restricted decrease in the protein and mRNA for the Aß-degrading enzyme neprilysin (NEP) was found, whereas various Aß-clearing and -degrading proteins were unchanged. Thus, we document multiple biochemical changes in the insulin-controlled DM monkey brain that can link DM with the risk of developing AD, including dysregulation of the insulin-signaling pathway, changes in tau phosphorylation, and a decrease in NEP expression in the hippocampus that is coupled with a localized increase in Aß. SIGNIFICANCE STATEMENT: Given that diabetes mellitus (DM) appears to increase the risk of developing Alzheimer's disease (AD), understanding the mechanisms by which DM promotes AD is important. We report that DM in a nonhuman primate brain leads to changes in the levels or posttranslational processing of proteins central to AD pathobiology, including tau, amyloid-ß (Aß), and the Aß-degrading protease neprilysin. Additional evidence from this model suggests that alterations in brain insulin signaling occurred that are reminiscent of insulin signaling pathway changes seen in human AD. Thus, in an in vivo model highly relevant to humans, we show multiple alterations in the brain resulting from DM that are mechanistically linked to AD risk.

Neurophysiological Effects of Bitopertin in Schizophrenia.

PURPOSE/BACKGROUND: Deficits in N-methyl-D-aspartate receptor (NMDAR) function contribute to symptoms and cognitive dysfunction in schizophrenia and are associated with impaired generation of event-related potential measures including auditory mismatch negativity. Parallel studies of the NMDAR agonist D-serine have suggested that sensitivity of these measures to glutamate-based interventions is related to symptomatic and cognitive response. Bitopertin is a selective inhibitor of glycine transport. This study investigates effects of bitopertin on NMDAR-related event-related potential deficits in schizophrenia. METHODS/PROCEDURES: Patients with schizophrenia/schizoaffective disorder were treated with bitopertin (10 mg, n = 29), in a double-blind, parallel group investigation. Auditory mismatch negativity served as primary outcome measures. Secondary measures included clinical symptoms and neurocognitive performance. FINDINGS/RESULTS: No significant changes were seen with bitopertin for neurophysiological, clinical, or neurocognitive assessments. IMPLICATIONS/CONCLUSIONS: These findings represent the first assessment of the effect of bitopertin on neurophysiological biomarkers. Bitopertin did not significantly affect either symptoms or NMDAR-related biomarkers at the dose tested (10 mg). Mismatch negativity showed high test-retest reliability, supporting its use as a target engagement measure.

History of sexual trauma moderates psychotherapy outcome for posttraumatic stress disorder.

BACKGROUND: Moderators of differential psychotherapy outcome for posttraumatic stress disorder (PTSD) are rare, yet have crucial clinical importance. We tested the moderating effects of trauma type for three psychotherapies in 110 unmedicated patients with chronic DSM-IV PTSD. METHODS: Patients were randomized to 14 weeks of prolonged exposure (PE, N = 38), interpersonal psychotherapy (IPT, N = 40), or relaxation therapy (RT, N = 32). The Clinician-Administered PTSD Scale (CAPS) was the primary outcome measure. Moderator candidates were trauma type: interpersonal, sexual, physical. We fit a regression model for week 14 CAPS as a function of treatment (a three-level factor), an indicator of trauma type presence/absence, and their interactions, controlling for baseline CAPS, and evaluated potential confounds. RESULTS: Thirty-nine (35%) patients reported sexual, 68 (62%) physical, and 102 (93%) interpersonal trauma. Baseline CAPS scores did not differ by presence/absence of trauma types. Sexual trauma as PTSD criterion A significantly moderated treatment effect: whereas all therapies had similar efficacy among nonsexually-traumatized patients, IPT had greater efficacy among sexually traumatized patients (efficacy difference with and without sexual trauma: IPT vs. PE and IPT vs. RT P's < .05), specifically in PTSD symptom clusters B and D (P's < .05). CONCLUSIONS: Few studies have assessed effects of varying trauma types on effects of differing psychotherapies. In this exploratory study, sexual trauma moderated PTSD outcomes of three therapies: IPT showed greater benefit for sexually traumatized patients than PE or RT. The IPT focuses on affect to help patients determine trust in their current environments may particularly benefit patients who have suffered sexual assault.

Can school counselors deliver cognitive-behavioral treatment for social anxiety effectively? A randomized controlled trial.

BACKGROUND: Social anxiety disorder (SAD) typically onsets in adolescence and is associated with multiple impairments. Despite promising clinical interventions, most socially anxious adolescents remain untreated. To address this clinical neglect, we developed a school-based, 12-week group intervention for youth with SAD, Skills for Academic and Social Success (SASS). When implemented by psychologists, SASS has been found effective. To promote dissemination and optimize treatment access, we tested whether school counselors could be effective treatment providers. METHOD: We randomized 138, ninth through 11th graders with SAD to one of three conditions: (a) SASS delivered by school counselors (C-SASS), (b) SASS delivered by psychologists (P-SASS), or (c) a control condition, Skills for Life (SFL), a nonspecific counseling program. Blind, independent, evaluations were conducted with parents and adolescents at baseline, post-intervention, and 5 months beyond treatment completion. We hypothesized that C-SASS and P-SASS would be superior to the control, immediately after treatment and at follow-up. No prediction was made about the relative efficacy of C-SASS and P-SASS. RESULTS: Compared to controls, adolescents treated with C-SASS or P-SASS experienced significantly greater improvement and reductions of anxiety at the end of treatment and follow-up. There were no significant differences between SASS delivered by school counselors and psychologists. CONCLUSION: With training, school counselors are effective treatment providers to adolescents with social anxiety, yielding benefits comparable to those obtained by specialized psychologists. Questions remain regarding means to maintain counselors' practice standards without external support.

Treatment decisions based on scalar and functional baseline covariates.

The amount and complexity of patient-level data being collected in randomized-controlled trials offer both opportunities and challenges for developing personalized rules for assigning treatment for a given disease or ailment. For example, trials examining treatments for major depressive disorder are not only collecting typical baseline data such as age, gender, or scores on various tests, but also data that measure the structure and function of the brain such as images from magnetic resonance imaging (MRI), functional MRI (fMRI), or electroencephalography (EEG). These latter types of data have an inherent structure and may be considered as functional data. We propose an approach that uses baseline covariates, both scalars and functions, to aid in the selection of an optimal treatment. In addition to providing information on which treatment should be selected for a new patient, the estimated regime has the potential to provide insight into the relationship between treatment response and the set of baseline covariates. Our approach can be viewed as an extension of "advantage learning" to include both scalar and functional covariates. We describe our method and how to implement it using existing software. Empirical performance of our method is evaluated with simulated data in a variety of settings and also applied to data arising from a study of patients with major depressive disorder from whom baseline scalar covariates as well as functional data from EEG are available.