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Introduction This study sought to assess the impact student pharmacist-led health outreach events had on participants in the Health Belief Model domains of perceived severity of disease, perceived barriers, perceived benefits, and self-efficacy. Methods This study was an observational pre‐/post-survey design conducted between January and December 2019 at student pharmacist-led community health outreach events in the Salt Lake City, Utah metropolitan area. The survey was developed partially based on the Health Belief Model and consisted of seven items with a five-point Likert scale (1 = strongly disagree to 5 = strongly agree). The survey was completed by participants before and immediately after engaging in the outreach event. Results A total of 31 participants across a variety of demographics and educational backgrounds completed the study. The surveys from the outreach events showed statistically significant increases in the participants' perceived severity of disease, perceived barriers, and self-efficacy. Perceived benefits was not significantly changed. Conclusions Student pharmacist-led community health outreach events significantly increase participants' perceived severity of disease, perceived barriers, and self-efficacy, which may indicate increased willingness to adopt the recommended health behavior.

The search for disease modifying therapies in Alzheimers disease (AD) has recently led to promising results but also revealed design issues in clinical trials themselves. Of particular importance is the potential statistical challenges that can arise when dosages change after an interim analysis, which is not uncommon in contemporary AD trials. Following the recent Aducanumab trials, we sought to study the implications of dose changes on the statistical power of an AD trial. We conducted extensive simulations to calculate statistical power when the relationship between treatment effect size and time is linear or non-linear, and the investigated drug has delayed treatment effect or not. Statistical power depends on many design factors including the dose change time, correlation, population homogeneity, and treatment effect time. We recommend that researchers conduct simulation studies at the interim analysis to justify the modified sample size and/or follow-up time modification meanwhile the type I and II error rates are controlled.

Background The etiology of freezing of gait in Parkinson’s disease (PD) is yet to be clarified. Non-motor risk factors including cognitive impairment, sleep disturbance and mood disorders have been shown in freezing of gait. Research question We aimed to determine the predictive value of non-motor features in freezing of gait development. Methods Data were obtained from the Parkinson’s Progression Markers Initiative. Fifty PD patients with self-reported freezing of gait, and 50 PD patients without freezing of gait at the fourth year visit were included. Groups were matched for Movement Disorders Society-Unified Parkinson’s Disease Rating Scale Part III scores. Several cognitive and non-cognitive tests were used for non-motor features at baseline and over time. Executive function, visuospatial function, processing speed, learning and memory tests were used for cognition. Non-cognitive tests included sleepiness, REM sleep behavior disorder, depression and anxiety scales. Results Patients with freezing of gait had higher scores on sleepiness, REM sleep behavior disorder, depression and anxiety scales. However, predictor model analysis revealed that baseline processing speed, learning and sleepiness scores were predictive of self-reported freezing of gait development over time. Significance Our findings suggest that specific cognitive deficits and sleep disorders are predictive of future freezing of gait. These features may be helpful in identifying underlying networks in freezing of gait and should be further investigated with neuroimaging studies.

Restricted mean survival time is an alternative measure of treatment effect to hazard ratio in clinical trials with time-to-event outcome. The current methods have been focused on one-stage designs. In this article, we propose optimal two-stage designs for a single-arm study with the smallest expected sample size. We compare the performance of the new optimal two-stage designs with the existing one-stage design with regards to the expected sample size and the expected total study length. The simulation results indicate that the new two-stage designs can save the expected sample size substantially as compared to the one-stage design. We use a non-small cell lung cancer trial to illustrate the application of the proposed designs. The proposed optimal two-stage designs are recommended for use when time for patient accrual is longer than the pre-specified follow-up time.

Background The underlying mechanisms for increased osteopenia and fracture rates in patients with diabetes are not well understood, but may relate to chronic systemic inflammation. We assessed the effect of treating periodontal disease (POD), a cause of chronic inflammation, on inflammatory and bone turnover markers in patients with diabetes. Materials and Methods Using an investigator-administered questionnaire, we screened a cross-section of patients presenting for routine outpatient diabetes care. We recruited 22 subjects with POD. Inflammatory and bone turnover markers were measured at baseline and 3 months following POD treatment (scaling, root planing and subantimicrobial dose doxycycline). Results There were nonsignificant reductions in high-sensitivity C-reactive protein (6.34–5.52 mg/L, P = 0.626) and tumor necrosis factor-alpha (10.37–10.01 pg/mL, P = 0.617). There were nonsignificant increases in urinary C-terminal telopeptide (85.50–90.23 pg/mL, P = 0.684) and bone-specific alkaline phosphatase (7.45–8.79 pg/mL, P = 0.074). Patients with >90% adherence with doxycycline were 6.4 times more likely to experience reduction in tumor necrosis factor-alpha ( P = 0.021) and 2.8 times more likely to experience reductions in high-sensitivity C-reactive protein ( P = 0.133). Conclusions Treatment of POD in patients with diabetes resulted in nonsignificant lowering of inflammatory markers and nonsignificant increase in bone turnover markers. However, adherence to doxycycline therapy resulted in better treatment effects.

We studied an unconditional approach based on partial maximization using different penalty values for comparing two incidence rates from Poisson distributions. We also consider a full maximization approach and an approach based on estimation and full maximization. Generally, the third approach has good performance.

We consider exact unconditional procedures for testing independence of exchangeable binary data with equal-sized clusters. The approximate unconditional approach is recommended as a basic procedure. The exact unconditional procedure based on estimation followed by maximization is recommended for the small number of clusters.

The Inverse Gaussian (IG) distribution is commonly introduced to model and examine right skewed data having positive support. When applying the IG model, it is critical to develop efficient goodness-of-fit tests. In this article, we propose a new test statistic for examining the IG goodness-of-fit based on approximating parametric likelihood ratios. The parametric likelihood ratio methodology is well-known to provide powerful likelihood ratio tests. In the nonparametric context, the classical empirical likelihood (EL) ratio method is often applied in order to efficiently approximate properties of parametric likelihoods, using an approach based on substituting empirical distribution functions for their population counterparts. The optimal parametric likelihood ratio approach is however based on density functions. We develop and analyze the EL ratio approach based on densities in order to test the IG model fit. We show that the proposed test is an improvement over the entropy-based goodness-of-fit test for IG presented by Mudholkar and Tian (2002). Theoretical support is obtained by proving consistency of the new test and an asymptotic proposition regarding the null distribution of the proposed test statistic. Monte Carlo simulations confirm the powerful properties of the proposed method. Real data examples demonstrate the applicability of the density-based EL ratio goodness-of-fit test for an IG assumption in practice.

Objective. To analyze the modifiable predictors of institution-wide residency match rates. Methods. This was a retrospective analysis of colleges and schools of pharmacy data and school-wide PGY-1 pharmacy residency match rates for 2013 through 2015. Independent variables included NAPLEX passing rates, history of ACPE probation, NIH funding, academic health center affiliation, dual-degree availability, program length, admit-to-applicant ratio, class size, tuition, student-driven research, clinically focused academic tracks, residency affiliation, U.S. News & World Report rankings, and minority enrollment. Results. In a repeated measures model, predictors of match results were NAPLEX pass rate, class size, academic health center affiliation, admit-to-applicant ratio, U.S. News & World Report rankings, and minority enrollment. Conclusion. Indicators of student achievement, college/school reputation, affiliations, and class demographics were significant predictors of institution-wide residency match rates. Further research is needed to understand how changes in these factors may influence overall match rates.