BACKGROUND To the authors' knowledge, limited data exist regarding long‐term quality of life (QOL) for patients diagnosed with intracranial meningioma. METHODS The data in the current study concerned 1722 meningioma cases diagnosed among residents of Connecticut, Massachusetts, California, Texas, and North Carolina from May 1, 2006 through March 14, 2013, and 1622 controls who were frequency matched to the cases by age, sex, and geography. These individuals were participants in a large, population‐based, case‐control study. Telephone interviews were used to collect data regarding QOL at the time of initial diagnosis or contact, using the Medical Outcomes Study Short‐Form 36 Health Survey. QOL outcomes were compared by case/control status. RESULTS Patients diagnosed with meningioma reported levels of physical, emotional, and mental health functioning below those reported in a general healthy population. Case participants and controls differed most significantly with regard to the domains of Physical and Social Functioning, Role‐Physical, Role‐Emotional, and Vitality. CONCLUSIONS In the current study, patients with meningioma experienced statistically significant decreases in QOL compared with healthy controls of a similar demographic breakdown, although these differences were found to vary in clinical significance. Cancer 2018;124:161‐6. © 2017 American Cancer Society.

In wake of the Covid-19 pandemic, 2019–2020 soccer seasons across the world were postponed and eventually made up during the summer months of 2020. Researchers from a variety of disciplines jumped at the opportunity to compare the rescheduled games, played in front of empty stadia, to previous games, played in front of fans. To date, most of this post-Covid soccer research has used linear regression models, or versions thereof, to estimate potential changes to the home advantage. However, we argue that leveraging the Poisson distribution would be more appropriate and use simulations to show that bivariate Poisson regression (Karlis and Ntzoufras in J R Stat Soc Ser D Stat 52(3):381–393, 2003) reduces absolute bias when estimating the home advantage benefit in a single season of soccer games, relative to linear regression, by almost 85%. Next, with data from 17 professional soccer leagues, we extend bivariate Poisson models estimate the change in home advantage due to games being played without fans. In contrast to current research that suggests a drop in the home advantage, our findings are mixed; in some leagues, evidence points to a decrease, while in others, the home advantage may have risen. Altogether, this suggests a more complex causal mechanism for the impact of fans on sporting events.

Background In neuro-oncology, traditional methods of enrolling the large numbers of participants required for studies of disease etiology and treatment response are costly, labor intensive, and may not include patients in regions without tumor registries. Methods In the Yale Acoustic Neuroma (AN) Study and International Low-Grade Glioma (LGG) Registry, we partnered with several brain tumor patient organizations to develop social media enrollment campaigns and use web-based data collection resources at the Yale University School of Public Health to test alternative methods to enroll neuro-oncology patients for epidemiologic study. Results In the AN study, we enrolled 1024 patients over 2 years. Of these, 865 patients completed the online questionnaire, 697 returned written consent, 583 sent a pathology report, and 569 returned a saliva specimen. The completed 569 participants did not differ by age or treatment from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) data but were more likely to be female (67% vs 52%) and white (94.8% vs 84%). Patients learned of the study through the Acoustic Neuroma Association (ANA) website (61.3%), ANA support group members (18%), and social media (primarily Facebook). Costs per patient enrolled were approximately 10% to 20% that of traditional registry-based enrollment methods. Results for the LGG study were similar. Conclusions Although additional effort will be required to ensure a diverse participant population, partnership with established patient organizations along with use of web-based technology and social media allowed for the successful enrollment of neuro-oncology patients at a fraction of the cost relative to traditional methods.

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Significant advancements have been made in recent years to optimize patient recruitment for clinical trials, however, improved methods for patient recruitment prediction are needed to support trial site selection and to estimate appropriate enrollment timelines in the trial design stage. In this paper, using data from thousands of historical clinical trials, we explore machine learning methods to predict the number of patients enrolled per month at a clinical trial site over the course of a trial's enrollment duration. We show that these methods can reduce the error that is observed with current industry standards and propose opportunities for further improvement.