Exact-corrected confidence interval for risk difference in noninferiority binomial trials.

A novel confidence interval estimator is proposed for the risk difference in noninferiority binomial trials. The proposed confidence interval, which is dependent on the prespecified noninferiority margin, is consistent with an exact unconditional test that preserves the type-I error and has improved power, particularly for smaller sample sizes, compared to the confidence interval by Chan and Zhang. The improved performance of the proposed confidence interval is theoretically justified and demonstrated with simulations and examples. An R package is also distributed that implements the proposed methods along with other confidence interval estimators.

Mental Health Screening in Pediatric Primary Care: Factors Associated With Screening Completion and Elevated Scores.

This study aimed to examine predictors of complete and elevated youth mental health screens. Parents of 4- to 11-year-old children completed the Strengths and Difficulties Questionnaire (SDQ) during a routine, universal mental health screening initiative in primary care. Bivariate logistic regressions were run to examine associations between independent (visit age, sex, race/ethnicity, language, insurance, and guardian) and dependent variables (screening completion and elevated SDQ score). Parents of younger and Spanish-speaking (vs English-speaking) children were less likely to have a complete SDQ screen. Among those with complete SDQ screens, older children, male children, those with public or no insurance, and those who had a mother (vs father) complete the screener were more likely to have an elevated score. Understanding patterns of screening completion rates and predictors of elevated screens provides valuable information to improve resource mapping and planning. Findings can inform mental health screening implementation and optimization within primary care.

Clinical Indoor Running Gait Analysis May Not Approximate Outdoor Running Gait Based on Novel Drone Technology.

BACKGROUND: Traditional running gait analysis is limited to artificial environments, but whether treadmill running approximates overground running is debated. This study aimed to compare treadmill gait analysis using fixed video with outdoor gait analysis using drone video capture. HYPOTHESIS: Measured kinematics would be similar between natural outdoor running and traditional treadmill gait analysis. STUDY DESIGN: Crossover study. LEVEL OF EVIDENCE: Level 2. METHODS: The study population included cross-country, track and field, and recreational athletes with current running mileage of at least 15 km per week. Participants completed segments in indoor and outdoor environments. Indoor running was completed on a treadmill with static video capture, and outdoor segments were obtained via drone on an outdoor track. Three reviewers independently performed clinical gait analysis on footage for 32 runners using kinematic measurements with published acceptable intra- and interrater reliability. RESULTS: Of the 8 kinematic variables measured, 2 were found to have moderate agreement indoor versus outdoor, while 6 had fair to poor agreement. Foot strike at initial contact and rearfoot position at midstance had moderate agreement indoor versus outdoor, with a kappa of 0.54 and 0.49, respectively. The remaining variables: tibial inclination at initial contact, knee flexion angle initial contact, forward trunk lean full gait cycle, knee center position midstance, knee separation midstance, and lateral pelvic drop at midstance were found to have fair to poor agreement, ranging from 0.21 to 0.36. CONCLUSION: This study suggests that kinematics may differ between natural outdoor running and traditional treadmill gait analysis. CLINICAL RELEVANCE: Providing recommendations for altering gait based on treadmill gait analysis may prove to be harmful if treadmill analysis does not approximate natural running environments. Drone technology could provide advancement in clinical running recommendations by capturing runners in natural environments.

Assessing the impact of COVID-19 on registered interventional clinical trials.

The coronavirus disease 2019 (COVID-19) pandemic has led to a dramatic impact worldwide and presented unprecedented challenges for clinical and translational medicine. We assess the impact of COVID-19 on submitted and completed interventional clinical trials that have been registered on ClinicalTrials.gov. After classifying over 85% of the registered clinical trials by their source, we carefully model the number of submitted and completed trials before and after March 2020. Overall, we find minimal impact of COVID-19 on the number of submitted clinical trials, although a much more substantial impact is observed for completed clinical trials. We also show that clinical trials with a pharmaceutical sponsor were more successful at completing trials during the pandemic compared to the trials with academic/hospital/government sponsors.

Some teaching resources using R with illustrative examples exploring COVID-19 data.

This article is presented in two parts: in the first part we discuss the use of R and R-related tools when implementing a data science curriculum in the classroom and direct readers to helpful R resources in education, and in the second part, we demonstrate the use of R in exploring COVID-19 data. In particular, we explore ethnic/racial distributions and COVID-19 death rates. Supplementary R markdown files are also included allowing all graphics to be easily reproduced. It is advised that this article be discussed with sensitivity and mindfulness to potentially vulnerable students, especially as there may be students who have lost relatives due to the COVID-19 virus.