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Broglio S.P.,University of Illinois at Urbana - Champaign | Schnebel B.,University of Oklahoma | Sosnoff J.J.,University of Illinois at Urbana - Champaign | Shin S.,University of Illinois at Urbana - Champaign | And 3 more authors.
Medicine and Science in Sports and Exercise | Year: 2010

Introduction: Sport concussion represents the majority of brain injuries occurring in the United States with 1.6-3.8 million cases annually. Understanding the biomechanical properties of this injury will support the development of better diagnostics and preventative techniques. Methods: We monitored all football related head impacts in 78 high school athletes (mean age = 16.7 yr) from 2005 to 2008 to better understand the biomechanical characteristics of concussive impacts. Results: Using the Head Impact Telemetry System, a total of 54,247 impacts were recorded, and 13 concussive episodes were captured for analysis. A classification and regression tree analysis of impacts indicated that rotational acceleration (>5582.3 rads-2), linear acceleration (>96.1g), and impact location (front, top, and back) yielded the highest predictive value of concussion. Conclusions: These threshold values are nearly identical with those reported at the collegiate and professional level. If the Head Impact Telemetry System were implemented for medical use, sideline personnel can expect to diagnose one of every five athletes with a concussion when the impact exceeds these tolerance levels. Why all athletes did not sustain a concussion when the impacts generated variables in excess of our threshold criteria is not entirely clear, although individual differences between participants may play a role. A similar threshold to concussion in adolescent athletes compared with their collegiate and professional counterparts suggests an equal concussion risk at all levels of play. Copyright © 2010 by the American College of Sports Medicine.

Jung S.-H.,Duke University | Young S.S.,National Institute of Statistical science
Journal of Biopharmaceutical Statistics | Year: 2012

Microarray is a technology to screen a large number of genes to discover those differentially expressed between clinical subtypes or different conditions of human diseases. Gene discovery using microarray data requires adjustment for the large-scale multiplicity of candidate genes. The family-wise error rate (FWER) has been widely chosen as a global type I error rate adjusting for the multiplicity. Typically in microarray data, the expression levels of different genes are correlated because of coexpressing genes and the common experimental conditions shared by the genes on each array. To accurately control the FWER, the statistical testing procedure should appropriately reflect the dependency among the genes. Permutation methods have been used for accurate control of the FWER in analyzing microarray data. It is important to calculate the required sample size at the design stage of a new (confirmatory) microarray study. Because of the high dimensionality and complexity of the correlation structure in microarray data, however, there have been no sample size calculation methods accurately reflecting the true correlation structure of real microarray data. We propose sample size and power calculation methods that are useful when pilot data are available to design a confirmatory experiment. If no pilot data are available, we recommend a two-stage sample size recalculation based on our proposed method using the first stage data as pilot data. The calculated sample sizes are shown to accurately maintain the power through simulations. A real data example is taken to illustrate the proposed method. © 2012 Copyright Taylor and Francis Group, LLC.

Wang X.,National Institute of Statistical science | Dey D.K.,University of Connecticut
Environmental and Ecological Statistics | Year: 2011

This paper introduces a flexible skewed link function for modeling ordinal response data with covariates based on the generalized extreme value (GEV) distribution. Commonly used probit, logit and complementary log-log links are prone to link misspecification because of their fixed skewness. The GEV link is flexible in fitting the skewness in the response curve with a free shape parameter. Using Bayesian methodology, it automatically detects the skewness in the response curve along with the model fitting. The flexibility of the proposed model is illustrated by its application to an ecological survey data about the coverage of Berberis thunbergii in New England. We employ the latent variable approach by Albert and Chib (J Am Stat Assoc 88:669-679, (1993) to develop computational schemes. For model selection, we employ the Deviance Information Criterion (DIC). © 2010 Springer Science+Business Media, LLC.

Zhou Y.,East China Normal University | Sedransk N.,National Institute of Statistical science
Statistics in Medicine | Year: 2013

Cardiac safety assessment in drug development concerns the ventricular repolarization (represented by electrocardiogram (ECG) T-wave) abnormalities of a cardiac cycle, which are widely believed to be linked with torsades de pointes, a potentially life-threatening arrhythmia. The most often used biomarker for such abnormalities is the prolongation of the QT interval, which relies on the correct annotation of onset of QRS complex and offset of T-wave on ECG. A new biomarker generated from a functional data-based methodology is developed to quantify the T-wave morphology changes from placebo to drug interventions. Comparisons of T-wave-form characters through a multivariate linear mixed model are made to assess cardiovascular risk of drugs. Data from a study with 60 subjects participating in a two-period placebo-controlled crossover trial with repeat ECGs obtained at baseline and 12 time points after interventions are used to illustrate this methodology; different types of wave form changes were characterized and motivated further investigation. © 2012 John Wiley & Sons, Ltd.

Agency: NSF | Branch: Standard Grant | Program: | Phase: SCIENCE RESOURCES STATISTICS | Award Amount: 224.90K | Year: 2013

This postdoctoral research program at the National Institute of Statistical Sciences (NISS) comprises performing innovative research and creating usable products that not only support the mission of the National Center for Science and Engineering Statistics (NCSES) but also address the needs of the nation.

From a technical perspective, the research is framed by two statistical themes and two key societal issues. The first statistical theme is characterization of uncertainties arising from novel methods of integrating and analyzing data, addressing a critical need in an era of declining data collection budgets and decreasing participation in government surveys. The second theme centers on conducting experiments with real data, simulating phenomena of interest in order to evaluate, and in some cases enable, methodological advances. Key issues regarding surveys, such as how many times and by what means to contact nonrespondents, are too complex to be treated analytically, and infeasible to address with real world experiments; therefore simulation is effectively the only laboratory available. Specific research topics include data integration, prediction, model to design feedback, data-quality-aware statistical disclosure limitation and cost data quality tradeoffs. All Federal statistical agencies stand to benefit from the research, which will produce innovative theory, novel, methodology and algorithmic implementations, together with datasets, analyses, software and insights that inform future data collections.

Broader Impacts: The societal issues are labor economics as it relates to the science, engineering and health workforce (SEHW). Understanding phenomena such as salaries, fringe benefits, mobility and training/job relationships is crucial to maintaining the United States competitiveness in a global economy, as well as to facing the challenges of difficult economic times. The second issue is aging, because other than the role of students born outside of the US, aging is the most important phenomenon taking place in the SEHW (and, arguably, in society as a whole). For both issues, understanding the dramatically increasing richness of observed behaviors within the SEHW is a profound opportunity. New kinds of family structures, shared positions, and an array of forms of post-first-retirement employment are among the central social trends of our times. This project will generate new insights that inform both future research and sound policy.

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