A novel biomarker selection method using multimodal neuroimaging data.

Identifying biomarkers is essential to obtain the optimal therapeutic benefit while treating patients with late-life depression (LLD). We compare LLD patients with healthy controls (HC) using resting-state functional magnetic resonance and diffusion tensor imaging data to identify neuroimaging biomarkers that may be potentially associated with the underlying pathophysiology of LLD. We implement a Bayesian multimodal local false discovery rate approach for functional connectivity, borrowing strength from structural connectivity to identify disrupted functional connectivity of LLD compared to HC. In the Bayesian framework, we develop an algorithm to control the overall false discovery rate of our findings. We compare our findings with the literature and show that our approach can better detect some regions never discovered before for LLD patients. The Hub of our discovery related to various neurobehavioral disorders can be used to develop behavioral interventions to treat LLD patients who do not respond to antidepressants.

Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies.

In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.

Longitudinal Changes in the Composition of the Penile Microbiome Are Associated With Circumcision Status, HIV and HSV-2 Status, Sexual Practices, and Female Partner Microbiome Composition.

Background: Penile microbiome composition has been associated with HSV-2 and HIV in men and with bacterial vaginosis (BV) and HSV-2 in female sex partners. This study sought to 1) characterize penile microbiome composition over a 1-year period and 2) identify factors associated with penile microbiome composition over time. Methods: This prospective study of community-recruited heterosexual couples in Kenya measured penile and vaginal microbiomes via 16S ribosomal RNA gene amplicon sequencing at 4 time points over 1 year (1, 6, and 12 months after baseline). We used longitudinal mixed-effects modeling to assess associated demographic, behavioral, and disease factors and changes in community type, meatal taxa with the highest mean relative abundance, and alpha and beta diversity measures. We estimated group-based trajectories to elucidate compositional trends. Results: Among 218 men with 740 observations, men had a median age of 26 years, 11.6% were living with HIV, and 46.1% were HSV-2 seropositive. We identified 7 penile community types that varied with circumcision status, female partner vaginal microbiome community state type (CST), condom use, and penile washing. Across varying analytic approaches, 50%-60% of men had stable penile microbiome compositions. Alpha diversity measures were lower for circumcised men and those who reported condom use; they were stable over time but higher if female partners had diverse CSTs or BV. BV was positively associated with the relative abundance of numerous individual penile taxa. The decreased Bray-Curtis similarity was more common for men with HSV-2, and HSV-2 was also associated with a lower relative abundance of Corynebacterium and Staphylococcus. Conclusions: Over a 1-year period, penile microbiome composition was stable for a substantial proportion of men and was influenced by men's circumcision status, sexual practices, female partner's vaginal CST and BV status, and men's HSV-2 status. In the female genital tract, a diverse CST is often associated with poorer health outcomes. Our results contribute toward understanding whether this framework extends to the penile microbiome and whether diversity and the associated penile microbiome compositions influence susceptibility or resilience to poorer health outcomes in men. Focusing on understanding how these factors influence the penile microbiome may lead to therapeutic avenues for reduced HSV-2 and BV infections in men and their female sex partners.

Feasibility of a Combined Neuromodulation and Yoga Intervention for Mild Traumatic Brain Injury and Chronic Pain: Protocol for an Open-label Pilot Trial.

BACKGROUND: Mild traumatic brain injury (mTBI) and chronic pain often co-occur and worsen rehabilitation outcomes. There is a need for improved multimodal nonpharmacologic treatments that could improve outcomes for both conditions. Yoga is a promising activity-based intervention for mTBI and chronic pain, and neuromodulation through transcranial magnetic stimulation is a promising noninvasive, nonpharmacological treatment for mTBI and chronic pain. Intermittent theta burst stimulation (iTBS) is a type of patterned, excitatory transcranial magnetic stimulation. iTBS can induce a window of neuroplasticity, making it ideally suited to boost the effects of treatments provided after it. Thus, iTBS may magnify the impacts of subsequently delivered interventions as compared to delivering those interventions alone and accordingly boost their impact on outcomes. OBJECTIVE: The aim of this study is to (1) develop a combined iTBS+yoga intervention for mTBI and chronic pain, (2) assess the intervention's feasibility and acceptability, and (3) gather preliminary clinical outcome data on quality of life, function, and pain that will guide future studies. METHODS: This is a mixed methods, pilot, open-labeled, within-subject intervention study. We will enroll 20 US military veteran participants. The combined iTBS+yoga intervention will be provided in small group settings once a week for 6 weeks. The yoga intervention will follow the LoveYourBrain yoga protocol-specifically developed for individuals with TBI. iTBS will be administered immediately prior to the LoveYourBrain yoga session. We will collect preliminary quantitative outcome data before and after the intervention related to quality of life (TBI-quality of life), function (Mayo-Portland Adaptability Index), and pain (Brief Pain Inventory) to inform larger studies. We will collect qualitative data via semistructured interviews focused on intervention acceptability after completion of the intervention. RESULTS: This study protocol was approved by Edward Hines Jr Veterans Administration Hospital Institutional Review Board (Hines IRB 1573116-4) and was prospectively registered on ClinicalTrials.gov (NCT04517604). This study includes a Food and Drug Administration Investigational Device Exemption (IDE: G200195). A 2-year research plan timeline was developed. As of March 2022, a total of 6 veterans have enrolled in the study. Data collection is ongoing and will be completed by November 2022. We expect the results of this study to be available by October 2024. CONCLUSIONS: We will be able to provide preliminary evidence of safety, feasibility, and acceptability of a novel combined iTBS and yoga intervention for mTBI and chronic pain-conditions with unmet treatment needs. TRIAL REGISTRATION: ClinicalTrials.gov NCT04517604; https://www.clinicaltrials.gov/ct2/show/NCT04517604. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/37836.

Vaginal and Penile Microbiome Associations With Herpes Simplex Virus Type 2 in Women and Their Male Sex Partners.

BACKGROUND: We determined how the vaginal and penile microbiomes contribute to herpes simplex virus type 2 (HSV-2) serostatus within sexual partnerships. METHODS: Microbiomes were characterized in cervicovaginal lavage and penile meatal swab specimens through high-throughput 16s ribosomal RNA gene amplicon sequencing. HSV-2 antibody was detected in serum specimens. We modeled vaginal and penile taxa and covariates contributing to HSV-2 status in women and men using bivariate probit analysis. RESULTS: Among 231 couples, HSV-2 was detected in both partners in 78 couples (33.8%), in the woman only in 52 (22.5%),in the man only in 27 (11.7%), and in neither in 74 (32.0%). Among the women (median age, 22 years) 10.9% had human immunodeficiency virus (HIV), and 21.4% had Bacterial vaginosis. Among men (median age, 26 years), 11.8% had HIV, and 55.0% circumcised. In an analysis with adjustment for sociodemographics and Bacterial vaginosis, enrichment of vaginal Gardnerella vaginalis and Lactobacillus iners was associated with increased likelihood of HSV-2 in both partners. Penile taxa (including Ureaplasma and Aerococcus) were associated with HSV-2 in women. CONCLUSIONS: We demonstrate that penile taxa are associated with HSV-2 in female partners, and vaginal taxa are associated with HSV-2 in male partners. Our findings suggest that couples-level joint consideration of genital microbiome and sexually transmitted infection or related outcomes could lead to new avenues for prevention.

Schistosomiasis is associated with rectal mucosal inflammation among Kenyan men who have sex with men.

Background:Schistosoma mansoni infection is hyperendemic in Lake Victoria communities and associated with cervicovaginal immune alterations and HIV acquisition. We assessed the hypothesis that schistosomiasis correlates with greater rectal inflammation in men who have sex with men (MSM) in Kisumu, Kenya. Methods: In this cross-sectional study of 38 HIV-negative MSM aged 18-35 years, schistosomiasis was diagnosed by urine circulating cathodic antigen (CCA). Microbiome was assessed in rectal swabs by 16S rRNA gene amplicon sequencing, and rectal inflammation by quartile normalized summative score of inflammatory cytokines (IL-1α, IL-1ß, IL-8, and TNF-α). Elastic net (EN) regression identified taxa associated with inflammation. Multivariable linear regression estimated the association between inflammation score and schistosomiasis and bacteria identified in EN. Results: Most men were CCA positive (24/38; 63%), and median rectal inflammation score was significantly higher in these participants (11 vs. 8, p = 0.04). In multivariable regression, CCA-positive men had 2.85-point greater inflammation score (p = 0.009). The relative abundance of Succinivibrio (coefficient = -1.13, p = 0.002) and Pseudomonas (coefficient = -1.04, p = 0.001) were negatively associated with inflammation. Discussion: CCA positivity was associated with rectal mucosal inflammation, controlling for rectal microbiome composition. Given its high prevalence and contribution to inflammation, schistosomiasis may have important implications for HIV transmission in this vulnerable population.

Generalized confidence interval for an agreement between raters.

Estimation and inference are two key components toward the solution of any statistical problem; however, the inferential issues of statistical assessment of agreement among two or more raters have not been well developed as compared to the development of estimation procedures in this area. The fundamental reason for this gap is the complex expression of the concordance correlation coefficient (CCC) that is frequently used in assessing agreement among raters. Large sample-based statistical tests for CCC often fail to produce desired results for small samples. Hence, inferential procedures for small samples are urgently needed to evaluate agreement between raters. We argue that hypothesis testing of CCC has little value in practice due to the absence of a gold standard of agreement. In this article, we construct the generalized confidence interval (GCI) for CCC utilizing a bivariate normal distribution of measurements, and also develop a large sample-based confidence interval (LSCI). We establish satisfactory performance of GCI by providing the desired coverage probability (CP) via simulation. Results of GCI and LSCI are illustrated and compared with a data set of a recent study performed at U.S. Department of Veterans Affairs, Hines.

A Pilot Trial Examining the Merits of Combining Amantadine and Repetitive Transcranial Magnetic Stimulation as an Intervention for Persons With Disordered Consciousness After TBI.

OBJECTIVE: Report pilot findings of neurobehavioral gains and network changes observed in persons with disordered consciousness (DoC) who received repetitive transcranial magnetic stimulation (rTMS) or amantadine (AMA), and then rTMS+AMA. PARTICIPANTS: Four persons with DoC 1 to 15 years after traumatic brain injury (TBI). DESIGN: Alternate treatment-order, within-subject, baseline-controlled trial. MAIN MEASURES: For group and individual neurobehavioral analyses, predetermined thresholds, based on mixed linear-effects models and conditional minimally detectable change, were used to define meaningful neurobehavioral change for the Disorders of Consciousness Scale-25 (DOCS) total and Auditory-Language measures. Resting-state functional connectivity (rsFC) of the default mode and 6 other networks was examined. RESULTS: Meaningful gains in DOCS total measures were observed for 75% of treatment segments and auditory-language gains were observed after rTMS, which doubled when rTMS preceded rTMS+AMA. Neurobehavioral changes were reflected in rsFC for language, salience, and sensorimotor networks. Between networks interactions were modulated, globally, after all treatments. CONCLUSIONS: For persons with DoC 1 to 15 years after TBI, meaningful neurobehavioral gains were observed after provision of rTMS, AMA, and rTMS+AMA. Sequencing and combining of treatments to modulate broad-scale neural activity, via differing mechanisms, merits investigation in a future study powered to determine efficacy of this approach to enabling neurobehavioral recovery.

Neural Connectivity Changes Facilitated by Familiar Auditory Sensory Training in Disordered Consciousness: A TBI Pilot Study.

For people with disordered consciousness (DoC) after traumatic brain injury (TBI), relationships between treatment-induced changes in neural connectivity and neurobehavioral recovery have not been explored. To begin building a body of evidence regarding the unique contributions of treatments to changes in neural network connectivity relative to neurobehavioral recovery, we conducted a pilot study to identify relationships meriting additional examination in future research. To address this objective, we examined previously unpublished neural connectivity data derived from a randomized clinical trial (RCT). We leveraged these data because treatment efficacy, in the RCT, was based on a comparison of a placebo control with a specific intervention, the familiar auditory sensory training (FAST) intervention, consisting of autobiographical auditory-linguistic stimuli. We selected a subgroup of RCT participants with high-quality imaging data (FAST n = 4 and placebo n = 4) to examine treatment-related changes in brain network connectivity and how and if these changes relate to neurobehavioral recovery. To discover promising relationships among the FAST intervention, changes in neural connectivity, and neurobehavioral recovery, we examined 26 brain regions and 19 white matter tracts associated with default mode, salience, attention, and language networks, as well as three neurobehavioral measures. Of the relationships discovered, the systematic filtering process yielded evidence supporting further investigation of the relationship among the FAST intervention, connectivity of the left inferior longitudinal fasciculus, and auditory-language skills. Evidence also suggests that future mechanistic research should focus on examining the possibility that the FAST supports connectivity changes by facilitating redistribution of brain resources. For a patient population with limited treatment options, the reported findings suggest that a simple, yet targeted, passive sensory stimulation treatment may have altered functional and structural connectivity. If replicated in future research, then these findings provide the foundation for characterizing the unique contributions of the FAST intervention and could inform development of new treatment strategies. For persons with severely damaged brain networks, this report represents a first step toward advancing understanding of the unique contributions of treatments to changing brain network connectivity and how these changes relate to neurobehavioral recovery for persons with DoC after TBI. Clinical Trial Registry: NCT00557076, The Efficacy of Familiar Voice Stimulation During Coma Recovery (http://www.clinicaltrials.gov).

Characteristics of Women and Their Male Sex Partners Predict Bacterial Vaginosis Among a Prospective Cohort of Kenyan Women With Nonoptimal Vaginal Microbiota.

BACKGROUND: Up to 50% of women with nonoptimal vaginal microbial community state type (CST) have bacterial vaginosis (BV). Little is known about what distinguishes women with and without BV diagnosis within nonoptimal CST. We identified features of women and their male sex partners associated with BV among women with nonoptimal vaginal CST. METHODS: In this prospective study, 252 heterosexual couples were observed at 1, 6, and 12 months after baseline. Microbiomes were characterized in cervicovaginal lavage and penile meatal swabs through high-throughput 16s ribosomal RNA gene amplicon sequencing. Nonoptimal CST was defined as CST-IV. Bacterial vaginosis was defined as a Nugent score of 7 to 10. Generalized estimating equation analysis estimated adjusted odds ratios (aORs) for BV among women with nonoptimal CST. RESULTS: At baseline, women with nonoptimal CST were a median age of 22 years, 44% had BV, 16% had HIV, and 66% had herpes simplex virus (HSV) type 2. Male partners were a median age of 27 years, 12% had HIV, 48% had HSV-2, and 55% were circumcised. Within nonoptimal CST, Sneathia sanguinegens, Prevotella species, Prevotella amnii, and Clostridiales, BV-associated bacteria-2 were statistically significantly enriched in observations with BV. In multivariable generalized estimating equation controlling for CST, HIV, and HSV-2, BV was increased among women with CST-IVA (aOR, 1.91; P = 0.087), HIV (aOR, 2.30; P = 0.051), HSV-2 (aOR, 1.75; P = 0.065), and enrichment of male partner penile taxa: Dialister (aOR, 1.16; P = 0.034), Megasphaera (aOR, 1.22; P = 0.001), and Brevibacterium (aOR, 1.13; P = 0.019).These results provide insights into factors differentiating women with BV among those with nonoptimal vaginal CST. Interrupting the sexual exchange of penile and vaginal taxa may be beneficial for preventing pathologic state of vaginal microbiome.

Predicting Autism Spectrum Disorder Using Domain-Adaptive Cross-Site Evaluation.

The advances in neuroimaging methods reveal that resting-state functional fMRI (rs-fMRI) connectivity measures can be potential diagnostic biomarkers for autism spectrum disorder (ASD). Recent data sharing projects help us replicating the robustness of these biomarkers in different acquisition conditions or preprocessing steps across larger numbers of individuals or sites. It is necessary to validate the previous results by using data from multiple sites by diminishing the site variations. We investigated partial least square regression (PLS), a domain adaptive method to adjust the effects of multicenter acquisition. A sparse Multivariate Pattern Analysis (MVVPA) framework in a leave one site out cross validation (LOSOCV) setting has been proposed to discriminate ASD from healthy controls using data from six sites in the Autism Brain Imaging Data Exchange (ABIDE). Classification features were obtained using 42 bilateral Brodmann areas without presupposing any prior hypothesis. Our results showed that using PLS, SVM showed poorer accuracies with highest accuracy achieved (62%) than without PLS but not significantly. The regions occurred in two or more informative connections are Dorsolateral Prefrontal Cortex, Somatosensory Association Cortex, Primary Auditory Cortex, Inferior Temporal Gyrus and Temporopolar area. These interrupted regions are involved in executive function, speech, visual perception, sense and language which are associated with ASD. Our findings may support early clinical diagnosis or risk determination by identifying neurobiological markers to distinguish between ASD and healthy controls.

Sample size determination for multilevel hierarchical designs using generalized linear mixed models.

A unified statistical methodology of sample size determination is developed for hierarchical designs that are frequently used in many areas, particularly in medical and health research studies. The solid foundation of the proposed methodology opens a new horizon for power analysis in presence of various conditions. Important features such as joint significance testing, unequal allocations of clusters across intervention groups, and differential attrition rates over follow up time points are integrated to address some useful questions that investigators often encounter while conducting such studies. Proposed methodology is shown to perform well in terms of maintaining type I error rates and achieving the target power under various conditions. Proposed method is also shown to be robust with respect to violation of distributional assumptions of random-effects.

Emerging Role of One-Carbon Metabolism and DNA Methylation Enrichment on δ-Containing GABAA Receptor Expression in the Cerebellum of Subjects with Alcohol Use Disorders (AUD).

Background: Cerebellum is an area of the brain particularly sensitive to the effects of acute and chronic alcohol consumption. Alcohol exposure decreases cerebellar Purkinje cell output by increasing GABA release from Golgi cells onto extrasynaptic α6/δ-containing GABAA receptors located on glutamatergic granule cells. Here, we studied whether chronic alcohol consumption induces changes in GABAA receptor subunit expression and whether these changes are associated with alterations in epigenetic mechanisms via DNA methylation. Methods: We used a cohort of postmortem cerebellum from control and chronic alcoholics, here defined as alcohol use disorders subjects (n=25/group). S-adenosyl-methionine/S-adenosyl-homocysteine were measured by high-performance liquid chromatography. mRNA levels of various genes were assessed by reverse transcriptase-quantitative polymerase chain reaction. Promoter methylation enrichment was assessed using methylated DNA immunoprecipitation and hydroxy-methylated DNA immunoprecipitation assays. Results: mRNAs encoding key enzymes of 1-carbon metabolism that determine the S-adenosyl-methionine/S-adenosyl-homocysteine ratio were increased, indicating higher "methylation index" in alcohol use disorder subjects. We found that increased methylation of the promoter of the δ subunit GABAA receptor was associated with reduced mRNA and protein levels in the cerebellum of alcohol use disorder subjects. No changes were observed in α1- or α6-containing GABAA receptor subunits. The expression of DNA-methyltransferases (1, 3A, and 3B) was unaltered, whereas the mRNA level of TET1, which participates in the DNA demethylation pathway, was decreased. Hence, increased methylation of the δ subunit GABAA receptor promoter may result from alcohol-induced reduction of DNA demethylation. Conclusion: Together, these results support the hypothesis that aberrant DNA methylation pathways may be involved in cerebellar pathophysiology of alcoholism. Furthermore, this work provides novel evidence for a central role of DNA methylation mechanisms in the alcohol-induced neuroadaptive changes of human cerebellar GABAA receptor function.

Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity.

Understanding abnormal resting-state functional connectivity of distributed brain networks may aid in probing and targeting mechanisms involved in major depressive disorder (MDD). To date, few studies have used resting state functional magnetic resonance imaging (rs-fMRI) to attempt to discriminate individuals with MDD from individuals without MDD, and to our knowledge no investigations have examined a remitted (r) population. In this study, we examined the efficiency of support vector machine (SVM) classifier to successfully discriminate rMDD individuals from healthy controls (HCs) in a narrow early-adult age range. We empirically evaluated four feature selection methods including multivariate Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net feature selection algorithms. Our results showed that SVM classification with Elastic Net feature selection achieved the highest classification accuracy of 76.1% (sensitivity of 81.5% and specificity of 68.9%) by leave-one-out cross-validation across subjects from a dataset consisting of 38 rMDD individuals and 29 healthy controls. The highest discriminating functional connections were between the left amygdala, left posterior cingulate cortex, bilateral dorso-lateral prefrontal cortex, and right ventral striatum. These appear to be key nodes in the etiopathophysiology of MDD, within and between default mode, salience and cognitive control networks. This technique demonstrates early promise for using rs-fMRI connectivity as a putative neurobiological marker capable of distinguishing between individuals with and without rMDD. These methods may be extended to periods of risk prior to illness onset, thereby allowing for earlier diagnosis, prevention, and intervention.

Divergent Influences of Cardiovascular Disease Risk Factor Domains on Cognition and Gray and White Matter Morphology.

OBJECTIVE: Hypertension, diabetes, dyslipidemia, and obesity are associated with preclinical alterations in cognition and brain structure; however, this often comes from studies of comprehensive risk scores or single isolated factors. We examined associations of empirically derived cardiovascular disease risk factor domains with cognition and brain structure. METHODS: A total of 124 adults (age, 59.8 [13.1] years; 41% African American; 50% women) underwent neuropsychological and cardiovascular assessments and structural magnetic resonance imaging. Principal component analysis of nine cardiovascular disease risk factors resulted in a four-component solution representing 1, cholesterol; 2, glucose dysregulation; 3, metabolic dysregulation; and 4, blood pressure. Separate linear regression models for learning, memory, executive functioning, and attention/information processing were performed, with all components entered at once, adjusting for age, sex, and education. MRI analyses included whole-brain cortical thickness and tract-based fractional anisotropy adjusted for age and sex. RESULTS: Higher blood pressure was associated with poorer learning (B = -0.19; p = .019), memory (B = -0.22; p = .005), and executive functioning performance (B = -0.14; p = .031), and lower cortical thickness within the right lateral occipital lobe. Elevated glucose dysregulation was associated with poorer attention/information processing performance (B = -0.21; p = .006) and lower fractional anisotropy in the right inferior and bilateral superior longitudinal fasciculi. Cholesterol was associated with higher cortical thickness within left caudal middle frontal cortex. Metabolic dysfunction was positively associated with right superior parietal lobe, left inferior parietal lobe, and left precuneus cortical thickness. CONCLUSIONS: Cardiovascular domains were associated with distinct cognitive, gray, and white matter alterations and distinct age groups. Future longitudinal studies may assist in identifying vulnerability profiles that may be most important for individuals with multiple cardiovascular disease risk factors.

The "Seven Pillars" Response to Patient Safety Incidents: Effects on Medical Liability Processes and Outcomes.

OBJECTIVE: To determine whether a communication and resolution approach to patient harm is associated with changes in medical liability processes and outcomes. DATA SOURCES/STUDY SETTING: Administrative, safety, and risk management data from the University of Illinois Hospital and Health Sciences System, from 2002 to 2014. STUDY DESIGN: Single health system, interrupted time series design. Using Mann-Whitney U tests and segmented regression models, we compared means and trends in incident reports, claims, event analyses, patient communication consults, legal fees, costs per claim, settlements, and self-insurance expenses before and after the implementation of the "Seven Pillars" communication and resolution intervention. DATA COLLECTION METHODS: Queried databases maintained by Department of Safety and Risk Management and the Department of Administrative Services at UIH. Extracted data from risk module of the Midas incident reporting system. PRINCIPAL FINDINGS: The intervention nearly doubled the number of incident reports, halved the number of claims, and reduced legal fees and costs as well as total costs per claim, settlement amounts, and self-insurance costs. CONCLUSIONS: A communication and optimal resolution (CANDOR) approach to adverse events was associated with long-lasting, clinically and financially significant changes in a large set of core medical liability process and outcome measures.

Detection and prediction limits for identifying highly confusable drug names from experimental data.

Confusions between drug names that look and sound alike are common, costly, harmful, and difficult to prevent. One prevention strategy is to screen proposed new drug names for confusability before approving them. Widespread acceptance of preapproval tests of confusability is compromised by the lack of experimental designs and statistical methods to support valid inferences about whether a proposed new name is unacceptably confusing. One way of identifying confusing names is to conduct memory and perception experiments on a set of drug names which would include both the new name and a set of control names (e.g., names already on the market). The experiment would yield an observed error rate for every name. Inferences about the acceptability of the new name can be made by comparing the error rate of the new name to the distribution of error rates of the control names. We describe four memory and perception experiments on drug names, carried out using clinicians as participants. Each experiment included drug names designated as test and control names. We demonstrate how to use a combination of logistic regression, Poisson prediction limits, and highly assured credible intervals to identify and apply a threshold for identifying unacceptably confusing names. Our models show an excellent fit to the data. These experimental designs and analytic methods should be useful in the preapproval testing of proposed new drug names and in similar regulatory scenarios where it is necessary to draw inferences about the comparative safety or effectiveness of new vs. old products.

Placebo-Controlled Trial of Familiar Auditory Sensory Training for Acute Severe Traumatic Brain Injury: A Preliminary Report.

BACKGROUND: Sensory stimulation is often provided to persons incurring severe traumatic brain injury (TBI), but therapeutic effects are unclear. OBJECTIVE: This preliminary study investigated neurobehavioral and neurophysiological effects related to sensory stimulation on global neurobehavioral functioning, arousal, and awareness. METHODS: A double-blind randomized placebo-controlled trial where 15 participants in states of disordered consciousness (DOC), an average of 70 days after TBI, were provided either the Familiar Auditory Sensory Training (FAST) or Placebo of silence. Global neurobehavioral functioning was measured with the Disorders of Consciousness Scale (DOCS). Arousal and awareness were measured with the Coma-Near-Coma (CNC) scale. Neurophysiological effect was measured using functional magnetic resonance imaging (fMRI). RESULTS: FAST (n = 8) and Placebo (n = 7) groups each showed neurobehavioral improvement. Mean DOCS change (FAST = 13.5, SD = 8.2; Placebo = 18.9, SD = 15.6) was not different, but FAST patients had significantly (P = .049; 95% confidence interval [CI] = -1.51, -.005) more CNC gains (FAST = 1.01, SD = 0.60; Placebo = 0.25, SD = 0.70). Mixed-effects models confirm CNC findings (P = .002). Treatment effect, based on CNC, is large (d = 1.88, 95% CI = 0.77, 3.00). Number needed to treat is 2. FAST patients had more fMRI activation in language regions and whole brain (P values <.05) resembling healthy controls' activation. CONCLUSIONS: For persons with DOC 29 to 170 days after TBI, FAST resulted in CNC gains and increased neural responsivity to vocal stimuli in language regions. Clinicians should consider providing the FAST to support patient engagement in neurorehabilitation.

Optimal Design and Purposeful Sampling: Complementary Methodologies for Implementation Research.

Optimal design has been an under-utilized methodology. However, it has significant real-world applications, particularly in mixed methods implementation research. We review the concept and demonstrate how it can be used to assess the sensitivity of design decisions and balance competing needs. For observational studies, this methodology enables selection of the most informative study units. For experimental studies, it entails selecting and assigning study units to intervention conditions in the most informative manner. We blend optimal design methods with purposeful sampling to show how these two concepts balance competing needs when there are multiple study aims, a common situation in implementation research.

Likelihood-Based Random-Effect Meta-Analysis of Binary Events.

Meta-analysis has been used extensively for evaluation of efficacy and safety of medical interventions. Its advantages and utilities are well known. However, recent studies have raised questions about the accuracy of the commonly used moment-based meta-analytic methods in general and for rare binary outcomes in particular. The issue is further complicated for studies with heterogeneous effect sizes. Likelihood-based mixed-effects modeling provides an alternative to moment-based methods such as inverse-variance weighted fixed- and random-effects estimators. In this article, we compare and contrast different mixed-effect modeling strategies in the context of meta-analysis. Their performance in estimation and testing of overall effect and heterogeneity are evaluated when combining results from studies with a binary outcome. Models that allow heterogeneity in both baseline rate and treatment effect across studies have low type I and type II error rates, and their estimates are the least biased among the models considered.