Magnitude of Urine Albumin and KIM-1 Changes Can be Used to Differentiate Glomerular Injury From Tubular Injury in Rats.

Emerging urinary kidney safety biomarkers have been evaluated in recent years and have been shown to be superior to the serum parameters blood urea nitrogen (BUN) and creatinine (sCr) for monitoring kidney injury in the proximal tubule. However, their potential application in differentiating the location of the initial kidney injury (eg, glomerulus vs tubule) has not been fully explored. Here, we assessed the performance of two algorithms that were constructed using either an empirical or a mathematical model to predict the site of kidney injury using a data set consisting of 22 rat kidney toxicity studies with known urine biomarker and histopathologic outcomes. Two kidney safety biomarkers used in both models, kidney injury molecule 1 (KIM-1) and albumin (ALB), were the best performers to differentiate glomerular injury from tubular injury. The performance of algorithms using these two biomarkers against the gold standard of kidney histopathologic examination showed high sensitivity in differentiating the location of the kidney damage to either the glomerulus or the proximal tubules. These data support the exploration of such an approach for use in clinical settings, leveraging urinary biomarker data to aid in the diagnosis of either glomerular or tubular injury where histopathologic assessments are not conducted.

A framework for testing non-inferiority in a three-arm, sequential, multiple assignment randomized trial.

Sequential multiple assignment randomized trial design is becoming increasingly used in the field of precision medicine. This design allows comparisons of sequences of adaptive interventions tailored to the individual patient. Superiority testing is usually the initial goal in order to determine which embedded adaptive intervention yields the best primary outcome on average. When direct superiority is not evident, yet an adaptive intervention poses other benefits, then non-inferiority testing is warranted. Non-inferiority testing in the sequential multiple assignment randomized trial setup is rather new and involves the specification of non-inferiority margin and other important assumptions that are often unverifiable internally. These challenges are not specific to sequential multiple assignment randomized trial and apply to two-arm non-inferiority trials that do not include a standard-of-care (or placebo) arm. To address some of these challenges, three-arm non-inferiority trials that include the standard-of-care arm are proposed. However, methods developed so far for three-arm non-inferiority trials are not sequential multiple assignment randomized trial-specific. This is because apart from embedded adaptive interventions, sequential multiple assignment randomized trial typically does not include a third standard-of-care arm. In this article, we consider a three-arm sequential multiple assignment randomized trial from an National Institutes of Health-funded study of symptom management strategies among people undergoing cancer treatment. Motivated by that example, we propose a novel data analytic method for non-inferiority testing in the framework of three-arm sequential multiple assignment randomized trial for the first time. Sample size and power considerations are discussed through extensive simulation studies to elucidate our method.

An integrated Bayesian framework for multi-omics prediction and classification.

With the growing commonality of multi-omics datasets, there is now increasing evidence that integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers that enable better disease outcome prediction and patient stratification. Several methods exist to perform host phenotype prediction from cross-sectional, single-omics data modalities but decentralized frameworks that jointly analyze multiple time-dependent omics data to highlight the integrative and dynamic impact of repeatedly measured biomarkers are currently limited. In this article, we propose a novel Bayesian ensemble method to consolidate prediction by combining information across several longitudinal and cross-sectional omics data layers. Unlike existing frequentist paradigms, our approach enables uncertainty quantification in prediction as well as interval estimation for a variety of quantities of interest based on posterior summaries. We apply our method to four published multi-omics datasets and demonstrate that it recapitulates known biology in addition to providing novel insights while also outperforming existing methods in estimation, prediction, and uncertainty quantification. Our open-source software is publicly available at https://github.com/himelmallick/IntegratedLearner.

A more powerful test for three-arm non-inferiority via risk difference: Frequentist and Bayesian approaches.

Necessity for finding improved intervention in many legacy therapeutic areas are of high priority. This has the potential to decrease the expense of medical care and poor outcomes for many patients. Typically, clinical efficacy is the primary evaluating criteria to measure any beneficial effect of a treatment. Albeit, there could be situations when several other factors (e.g. side-effects, cost-burden, less debilitating, less intensive, etc.) which can permit some slightly less efficacious treatment options favorable to a subgroup of patients. This often leads to non-inferiority (NI) testing. NI trials may or may not include a placebo arm due to ethical reasons. However, when included, the resulting three-arm trial is more prudent since it requires less stringent assumptions compared to a two-arm placebo-free trial. In this article, we consider both Frequentist and Bayesian procedures for testing NI in the three-arm trial with binary outcomes when the functional of interest is risk difference. An improved Frequentist approach is proposed first, which is then followed by a Bayesian counterpart. Bayesian methods have a natural advantage in many active-control trials, including NI trial, as it can seamlessly integrate substantial prior information. In addition, we discuss sample size calculation and draw an interesting connection between the two paradigms.

Impact of substance use disorders on employment for veterans.

Approximately 1.1 million veterans meet criteria for substance use disorders (SUDs) with 1 in 4 struggling with illicit drugs, 4 in 5 struggling with alcohol use, and 1 in 13 struggling with both. The purpose of this study was to examine the impact of SUDs on closure status (exiting with employment, did not exit with employment) for veterans served in a Department of Veterans Affairs' Veterans Health Administration (VHA) vocational rehabilitation (VR) program. Data (N = 2,620) from a VHA VR program in the Veterans Integrated Service Network 12 network were obtained for the purpose of the present study and consisted of veterans enrolled from 2012 to 2018. Findings showed that veterans without SUDs were more likely to exit with employment, and veterans enrolled in transitional work and community-based employment were more likely to exit with employment compared to those within supported employment (SE). Given that SE helps to serve veterans with the most severe psychological or medical conditions (e.g., active psychosis, schizophrenia, bipolar disorder, spinal cord injury, traumatic brain injury), findings suggest veterans are more successful with less serious mental health conditions. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

New approaches for testing non-inferiority for three-arm trials with Poisson distributed outcomes.

With the availability of limited resources, innovation for improved statistical method for the design and analysis of randomized controlled trials (RCTs) is of paramount importance for newer and better treatment discovery for any therapeutic area. Although clinical efficacy is almost always the primary evaluating criteria to measure any beneficial effect of a treatment, there are several important other factors (e.g., side effects, cost burden, less debilitating, less intensive, etc.), which can permit some less efficacious treatment options favorable to a subgroup of patients. This leads to non-inferiority (NI) testing. The objective of NI trial is to show that an experimental treatment is not worse than an active reference treatment by more than a pre-specified margin. Traditional NI trials do not include a placebo arm for ethical reason; however, this necessitates stringent and often unverifiable assumptions. On the other hand, three-arm NI trials consisting of placebo, reference, and experimental treatment, can simultaneously test the superiority of the reference over placebo and NI of experimental treatment over the reference. In this article, we proposed both novel Frequentist and Bayesian procedures for testing NI in the three-arm trial with Poisson distributed count outcome. RCTs with count data as the primary outcome are quite common in various disease areas such as lesion count in cancer trials, relapses in multiple sclerosis, dermatology, neurology, cardiovascular research, adverse event count, etc. We first propose an improved Frequentist approach, which is then followed by it's Bayesian version. Bayesian methods have natural advantage in any active-control trials, including NI trial when substantial historical information is available for placebo and established reference treatment. In addition, we discuss sample size calculation and draw an interesting connection between the two paradigms.

The reciprocal Bayesian LASSO.

A reciprocal LASSO (rLASSO) regularization employs a decreasing penalty function as opposed to conventional penalization approaches that use increasing penalties on the coefficients, leading to stronger parsimony and superior model selection relative to traditional shrinkage methods. Here we consider a fully Bayesian formulation of the rLASSO problem, which is based on the observation that the rLASSO estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters are assigned independent inverse Laplace priors. Bayesian inference from this posterior is possible using an expanded hierarchy motivated by a scale mixture of double Pareto or truncated normal distributions. On simulated and real datasets, we show that the Bayesian formulation outperforms its classical cousin in estimation, prediction, and variable selection across a wide range of scenarios while offering the advantage of posterior inference. Finally, we discuss other variants of this new approach and provide a unified framework for variable selection using flexible reciprocal penalties. All methods described in this article are publicly available as an R package at: https://github.com/himelmallick/BayesRecipe.

The mediating effects of quality of life, depression, and generalized anxiety on perceived barriers to employment success for people diagnosed with Neurofibromatosis Type 1.

BACKGROUND: Neurofibromatosis Type 1 (NF1) is a genetic disorder that presents with physical symptoms that can negatively impact numerous areas of one's life, including occupational and psychological functioning, with decreased quality of life compared to a normative population. The purpose of the current study was to explore differences in the impact of psychological factors (anxiety and depression), quality of life and employment hope on barriers to successful employment between those with NF1 and matched controls. METHODS: A total of 212 individuals were stratified into two groups (NF1 and matched controls) using a cross-sectional design that collected a one-time response. RESULTS: A mediation analysis in which total barriers to successful employment on the differences between groups with quality of life, anxiety and depression as the mediators, and levels of employment hope as the co-variates were examined. The results confirmed a direct (.001) and indirect (< .001) relationship between barriers to successful employment with NF1 to matched controls, and with quality of life, anxiety, and depression. CONCLUSIONS: The current findings indicate that the barriers to successful employment for individuals with NF1 impact their quality of life, anxiety, and depression more than that of the matched controls. Poorer barriers of employment observed amongst people with a genetic disease can impact mental health and quality of life.

A Microdose Cocktail to Evaluate Drug Interactions in Patients with Renal Impairment.

Renal impairment (RI) is known to influence the pharmacokinetics of nonrenally eliminated drugs, although the mechanism and clinical impact is poorly understood. We assessed the impact of RI and single dose oral rifampin (RIF) on the pharmacokinetics of CYP3A, OATP1B, P-gp, and BCRP substrates using a microdose cocktail and OATP1B endogenous biomarkers. RI alone had no impact on midazolam (MDZ), maximum plasma concentration (Cmax ), and area under the curve (AUC), but a progressive increase in AUC with RI severity for dabigatran (DABI), and up to ~2-fold higher AUC for pitavastatin (PTV), rosuvastatin (RSV), and atorvastatin (ATV) for all degrees of RI was observed. RIF did not impact MDZ, had a progressively smaller DABI drug-drug interaction (DDI) with increasing RI severity, a similar 3.1-fold to 4.4-fold increase in PTV and RSV AUC in healthy volunteers and patients with RI, and a diminishing DDI with RI severity from 6.1-fold to 4.7-fold for ATV. Endogenous biomarkers of OATP1B (bilirubin, coproporphyrin I/III, and sulfated bile salts) were generally not impacted by RI, and RIF effects on these biomarkers in RI were comparable or larger than those in healthy volunteers. The lack of a trend with RI severity of PTV and several OATP1B biomarkers, suggests that mechanisms beyond RI directly impacting OATP1B activity could also be considered. The DABI, RSV, and ATV data suggest an impact of RI on intestinal P-gp, and potentially BCRP activity. Therefore, DDI data from healthy volunteers may represent a worst-case scenario for clinically derisking P-gp and BCRP substrates in the setting of RI.

Gaming and Gaming Disorder: A Mediation Model Gender, Salience, Age of Gaming Onset, and Time Spent Gaming.

Females in empirically based peer-reviewed studies of Internet gaming disorder (IGD) are underrepresented, despite evidence that there are only minor gender disparities present in online gaming. Moreover, few studies have specifically evaluated adult gender effects, within a formal diagnosis of IGD, and behavioral motivation, as defined by the reinforcing behavioral function. A mediation analysis evaluated the relationship between gender, behavioral motivation, and the diagnostic features in online gaming among adults to understand the impact of motivation on videogame playing. This study interviewed 304 adults (aged >18 years) in which 178 identified as female. Participants completed the Video Game Functional Assessment-Revised (VGFA-R) and the 20-item Internet Gaming Disorder Test (IGDT-20) through an online survey. Results showed that number of hours played per week, and subfactors of the VGFA-R differed between gender, indicating that the function and the maintaining of videogame play are essential in evaluating videogame addiction. These findings support and extend the literature's limited findings concerning gender and online gaming.

Comparison of the Video Game Functional Assessment-Revised (VGFA-R) and Internet Gaming Disorder Test (IGD-20).

Initially labeled as internet addiction in the mid-1990s (e.g., Griffiths, 1996; Young, 1996), researchers have since focused on how specific online activities result in negative consequences for those who overuse and have problems with online applications such as online gambling and online sex (Griffiths, 2000; Potenza, 2017). More recently, this has been applied to online problematic video game play, often used synonymously with terms such as online video game addiction, online gaming addiction, and Internet gaming disorder (IGD). With the publication of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association [APA], 2013), IGD was identified by the APA as warranting further study. The current proposed diagnostic criterion in the DSM-5 requires the presence of five of nine symptoms over a 12-month period. These include: (a) preoccupation or obsession with Internet games, (b) withdrawal symptoms when not playing Internet games, (c) an increasing need over time to spend more and more time playing video games, (d) failed attempts to stop or curb Internet gaming, (e) loss of interest in other activities such as hobbies, (f) continued overuse of Internet games even with knowledge of the impact of overuse on their life, (g) lying about extent of Internet game usage, (h) uses Internet games to relieve anxiety or guilt, and (i) has lost or put at risk an opportunity or relationship because of Internet games (American Psychiatric Association [APA], 2013). However, it is unclear if the disorder represents addiction to the internet or if IGD evaluates specific behaviors occurring within the context of the video gaming (Starcevic and Billieux, 2017; Young and Brand, 2017).

Rehabilitation technology services and employment outcomes among consumers using division of rehabilitation services.

Purpose: The primary goal of the study was to evaluate how the use of rehabilitation technology impacted closure status for consumers receiving services in fiscal year (FY) 2014. Materials and Methods: Rehabilitation Service Administration (RSA-911) Case Service Report FY 2014 archival dataset was obtained from the U.S. Department of Education (2014) and secondary analyses was performed for this study. RSA-911 archival data is updated on an annual basis and consists of all state-federal rehabilitation consumers who were served in the specific fiscal year. The dataset contains information related to each consumer's demographic information (e.g. age, gender, race) and other supplemental information (e.g. weekly earnings at closure, cause of disability, services provided). Results and Conclusions: A multiple logistic regression analysis was utilized and revealed that white consumers receiving rehabilitation technology (RT) services have significantly higher closure rate than consumers of other races, RT services differ by the employment status at application, RT services differ by the type of disability, educational level at application for people receiving RT services did predict closure status (i.e. exiting with an employment outcome), IEP status did not predict closure status, weekly earnings at application did predict closure status and the interaction effect between IEP and RT services is statistically significant. The odds ratio (ORs) were presented at the 95% confidence interval (CI). Vocational rehabilitation counselors needs training to correctly identify appropriate RT services for consumers, so that the likelihood of exiting with an employment outcome is obtained. Implications for Rehabilitation RT services significantly improved their chances of successful employment compared to those who did not receive RT services. Education at closure would also have some significant impact on employment outcomes. Training in Assistive Technology (AT) for Vocational Rehabilitation counselors will assist in the proper identification of AT requirements, which may lead to a higher likelihood of consumers exiting with an employment outcome.