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Background: Mixed martial arts (MMA) fighters, due to exposure to repetitive head impacts, are at risk for brain atrophy and neurodegenerative sequelae. Simultaneously, motor skills training and cognition-rich activities have been linked with larger regional brain volumes. The majority of an MMA fighter's sporting activity occurs during practice (e.g., sparring) rather than formal competition. This study, therefore, aims to be the first to explore regional brain volumes associated with sparring in MMA fighters. Methods: Ninety-four active, professional MMA fighters from the Professional Fighters Brain Health Study met inclusion criteria for this cross-sectional analysis. Adjusted multivariable regression analyses were utilized to examine the relationship between the number of sparring practice rounds per week during typical training and a select number of regional brain volumes (i.e., caudate, thalamus, putamen, hippocampus, amygdala). Results: A higher number of weekly sparring rounds during training was significantly associated with larger left (beta = 13.5 µL/round, 95% CI 2.26–24.8) and right (beta = 14.9 µL/round, 95% CI 3.64–26.2) caudate volumes. Sparring was not significantly associated with left or right thalamus, putamen, hippocampus, or amygdala volumes. Conclusions: More weekly rounds of sparring was not significantly associated with smaller volumes in any of the brain regions studied in active, professional MMA fighters. Sparring's significant association with larger caudate volume raises questions about whether fighters who spar more experience attenuated trauma-related decreases in caudate volume relative to fighters who spar less, whether fighters who spar more experience minimal or even positive changes to caudate volume, whether baseline differences in caudate size may have mediated results, or whether some other mechanism may be at play. Given limitations inherent to the cross-sectional study design, more research is needed to further explore the brain effects of sparring in MMA. [ABSTRACT FROM AUTHOR]

Confidence interval for the difference of two proportions has been studied for decades. Many methods were developed to improve the approximation of the limiting distribution of test statistics, such as the profile likelihood method, the score method, and the Wilson method. For the Wilson interval developed by Beal (Biometrics 43:941, 1987), the approximation of the Z test statistic to the standard normal distribution may be further improved by utilizing the continuity correction, in the observation of anti-conservative intervals from the Wilson interval. We theoretically prove that the Wilson interval is nested in the continuity corrected Wilson interval under mild conditions. We compare the continuity corrected Wilson interval with the commonly used methods with regards to coverage probability, interval width, and mean squared error of coverage probability. The proposed interval has good performance in many configurations. An example from a Phase II cancer trial is used to illustrate the application of these methods. [ABSTRACT FROM AUTHOR]

Biomedical studies, such as clinical trials, often require the comparison of measurements from two correlated tests in which each unit of observation is associated with a binary outcome of interest via relative risk. The associated confidence interval is crucial because it provides an appreciation of the spectrum of possible values, allowing for a more robust interpretation of relative risk. Of the available confidence interval methods for relative risk, the asymptotic score interval is the most widely recommended for practical use. We propose a modified score interval for relative risk and we also extend an existing nonparametric U-statistic-based confidence interval to relative risk. In addition, we theoretically prove that the original asymptotic score interval is equivalent to the constrained maximum likelihood-based interval proposed by Nam and Blackwelder. Two clinically relevant oncology trials are used to demonstrate the real-world performance of our methods. The finite sample properties of the new approaches, the current standard of practice, and other alternatives are studied via extensive simulation studies. We show that, as the strength of correlation increases, when the sample size is not too large the new score-based intervals outperform the existing intervals in terms of coverage probability. Moreover, our results indicate that the new nonparametric interval provides the coverage that most consistently meets or exceeds the nominal coverage probability. [ABSTRACT FROM AUTHOR]

Background: Severe headaches are common after subarachnoid hemorrhage. Guidelines recommend treatment with acetaminophen and opioids, but patient data show that headaches often persist despite multimodal treatment approaches. Considering an overall slim body of data for a common complaint affecting patients with SAH during their intensive care stay, we set out to assess practice patterns in headache management among clinicians who treat patients with SAH. Methods: We conducted an international cross-sectional study through a 37-question Web-based survey distributed to members of five professional societies relevant to intensive and neurocritical care from November 2021 to January 2022. Responses were characterized through descriptive analyses. Fisher's exact test was used to test associations. Results: Of 516 respondents, 329 of 497 (66%) were from North America and 121 of 497 (24%) from Europe. Of 435 respondents, 379 (87%) reported headache as a major management concern for patients with SAH. Intensive care teams were primarily responsible for analgesia during hospitalization (249 of 435, 57%), whereas responsibility shifted to neurosurgery at discharge (233 of 501, 47%). Most used medications were acetaminophen (90%), opioids (66%), corticosteroids (28%), and antiseizure medications (28%). Opioids or medication combinations including opioids were most frequently perceived as most effective by 169 of 433 respondents (39%, predominantly intensivists), followed by corticosteroids or combinations with corticosteroids (96 of 433, 22%, predominantly neurologists). Of medications prescribed at discharge, acetaminophen was most common (303 of 381, 80%), followed by opioids (175 of 381, 46%) and antiseizure medications (173 of 381, 45%). Opioids during hospitalization were significantly more prescribed by intensivists, by providers managing higher numbers of patients with SAH, and in Europe. At discharge, opioids were more frequently prescribed in North America. Of 435 respondents, 299 (69%) indicated no change in prescription practice of opioids with the opioid crisis. Additional differences in prescription patterns between continents and providers and while inpatient versus at discharge were found. Conclusions: Post-SAH headache in the intensive care setting is a major clinical concern. Analgesia heavily relies on opioids both in use and in perception of efficacy, with no reported change in prescription patterns for opioids for most providers despite the significant drawbacks of opioids. Responsibility for analgesia shifts between hospitalization and discharge. International and provider-related differences are evident. Novel treatment strategies and alignment of prescription between providers are urgently needed. [ABSTRACT FROM AUTHOR]

Repeated measures designs are widely used in practice to increase power, reduce sample size, and increase efficiency in data collection. Correlation between repeated measurements is one of the first research questions that needs to be addressed in a repeated-measure study. In addition to an estimate for correlation, confidence interval should be computed and reported for statistical inference. The asymptotic interval based on the delta method is traditionally calculated due to its simplicity. However, this interval is often criticized for its unsatisfactory performance with regards to coverage and interval width. Bootstrap could be utilized to reduce the interval width, and the widely used bootstrap intervals include the percentile interval, the bias-corrected interval, and the bias-corrected with acceleration interval. Wilcox (Comput Stat Data Anal 22:89–98,1996) suggested a modified percentile interval with the interval levels adjusted by sample size to have the coverage probability close to the nominal level. For a study with repeated measures, more parameters in addition to sample size would affect the coverage probability. For these reasons, we propose modifying the percentiles in the percentile interval to guarantee the coverage probability based on simulation studies. We analyze the correlation between imaging volumes and memory scores from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study to illustrate the application of the considered intervals. The proposed interval is exact with the coverage probability guaranteed, and is recommended for use in practice. [ABSTRACT FROM AUTHOR]

Amyloid- β (A β ) is the target in many clinical trials for Alzheimer's disease (AD). Preclinical AD patients are heterogeneous with regards to different backgrounds and diagnosis. Accurately predicting A β status of participants by using machine learning (ML) models based on easily accessible data, could improve the effectiveness of AD clinical trials. We will develop optimal ML models for each subpopulation stratified by sex and disease stages using sub scores from screening neurological tests. Data from the AD Neuroimaging Initiative (ADNI) were used to build the ML models, for three groups: individuals with significant memory concern, early mild cognitive impairment (MCI), and late MCI. Data were further separated into 6 groups by disease stage (3 levels) and sex (2 categories). The outcome was defined as the A β status confirmed by the PET imaging, and the features include demographic data, newly identified risk factors, screening tests, and the domain scores from screening tests. Monte Carlo simulation studies were used together with k-fold cross-validation technique to compute model performance metric. We also develop a new feature selection method based on the stochastic ordering to avoiding searching all possible combinations of features. Accuracy of the identified optimal model for SMC male was over 90% by using domain scores, and accuracy for LMCI female was above 86%. Domain scores can improve the ML model prediction as compared to the total scores. Accurate ML prediction models can identify the proper population for AD clinical trials. [ABSTRACT FROM AUTHOR]

Bilateral data are frequently occur in medical research. Asymptotic approaches are traditionally used to construct confidence intervals for proportion difference. However, they are often have unsatisfactory performance with regards to coverage, with the actual coverage below the nominal level or being too conservative. For these reasons, we propose developing exact one-sided limits for proportion difference in a parallel study with bilateral data to guarantee the coverage probability when sample size is small to medium. A statistical quantity has to be used for sample space ordering in the exact limit calculation. Four asymptotic limits are utilized as statistical quantities: the Wald limits under the independence or dependence assumptions for variance estimates, the Wald limits with the difference estimate under the dependence assumption, and the bootstrap percentile limits. We compare the performance of these exact limits with regards to average length and the limits of all possible samples. A real example from a randomized clinical trial in otolaryngology is used to illustrate the application of the proposed exact limits. [ABSTRACT FROM AUTHOR]

In contemporary clinical trials, often evaluated simultaneously are multiple new treatments or the same treatment at multiple dose levels. These treatments are first compared with a control, and the best candidate with sufficient activity is then picked for the following trial for further investigation. When the primary outcome is binary, several testing procedures including Dunnett's test, have been proposed for the assessment of hypotheses. The sample size of each group is predetermined; thus, an unconditional exact approach is aligned with the study design. The exact unconditional approach based on maximization has been studied for comparing multiple treatments with a control. The newly developed exact unconditional approach based on estimation and maximization could possibly increase the effectiveness of exact approaches by smoothing the tail probability surface. We compare these 2 exact unconditional approaches based on 3 commonly used test statistics under various design settings. Based on results from numerical studies, we provide recommendations on the usage of these exact approaches. A real clinical trial to treat psoriasis is used to illustrate the application of the considered exact approaches. [ABSTRACT FROM AUTHOR]

We present sample size determination using exact approaches for testing the ratio of incidence rates from a two-arm non-inferiority trial. The first approach is the exact conditional approach by treating the total number of events from two groups fixed, and the other is the exact unconditional approach based on maximization. Exact approaches guarantee the type I error rate which is often not satisfied in the commonly used asymptotic approaches on the basis of the corresponding limiting distributions of test statistics. We provide tables for sample size determination in commonly used cases in practice, and an example is used to show the application of these exact approaches. Sample size comparison using different exact approaches concludes that the exact unconditional approach based on the Wald-type test statistic has good performance in balanced studies. [ABSTRACT FROM AUTHOR]

In a one-sided hypothesis testing problem in clinical trials, the monotonic condition of a tail probability function is fundamentally important to guarantee that the actual type I and II error rates occur at the boundary of their associated parameter spaces. Otherwise, one has to search for the actual rates over the complete parameter space, which could be very computationally intensive. This important property has been extensively studied in traditional one-stage study settings (e.g., non-inferiority or superiority between two binomial proportions), but there is very limited research for this property in a two-stage design setting, e.g., Simon's two-stage design. In this note, we theoretically prove that the tail probability is an increasing function of the parameter in Simon's two-stage design. This proof not only provides theoretical justification that p-value occurs at the boundary of the parameter space, but also helps to reduce the computational intensity for study design search. [ABSTRACT FROM AUTHOR]

Four testing procedures are considered for testing the response rate of one sample correlated binary data with a cluster size of one or two, which often occurs in otolaryngologic and ophthalmologic studies. Although an asymptotic approach is often used for statistical inference, it is criticized for unsatisfactory type I error control in small sample settings. An alternative to the asymptotic approach is an unconditional approach. The first unconditional approach is the one based on estimation, also known as parametric bootstrap (Lee and Young in Stat Probab Lett 71(2):143-153, ). The other two unconditional approaches considered in this article are an approach based on maximization (Basu in J Am Stat Assoc 72(358):355-366, ), and an approach based on estimation and maximization (Lloyd in Biometrics 64(3):716-723, ). These two unconditional approaches guarantee the test size and are generally more reliable than the asymptotic approach. We compare these four approaches in conjunction with a test proposed by Lee and Dubin (Stat Med 13(12):1241-1252, ) and a likelihood ratio test derived in this article, in regards to type I error rate and power for sample sizes from small to medium. An example from an otolaryngologic study is provided to illustrate the various testing procedures. The unconditional approach based on estimation and maximization using the test in Lee and Dubin (Stat Med 13(12):1241-1252, ) is preferable due to the power advantageous. [ABSTRACT FROM AUTHOR]