Rick Chappell (radiotherapy, dose-finding studies, clinical trials)
I work in several areas of statistics as applied to medical research. I develop and fit models of how radiation damages tumors and normal tissue for patients with cancer. Since tumor cells respond differently to radiation than other cells, this difference can be exploited to maximize the damage to one while minimizing harm to the other. I also am interested in the design of studies that determine the proper dose of drugs or radiation to give to patients with cancer. In addition, I am involved in research into various other aspects of clinical trials. |
Thomas Cook (clinical trials, survival analysis, interim monitoring)
My research focuses on most statistical aspects of randomized controlled clinical trials. Specific areas include study design, especially sample size and mid-course corrections, data reporting and presentation, and interim monitoring for safety and efficacy. Delays in reporting or classification of study events such as recurrent heart attacks or cardiac procedures can affect the analysis, and I have studied methods of accounting for such delays. |
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Steenbock Professor of Biomolecular Structure,
and Director of the National Magnetic Resonance
Facility at Madison and the BioMagResBank,
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Latent variable and structural equation modeling; measurement error models; multivariate analysis; hierarchical and mixture models; application in cancer research. |
Mark Craven (bioinformatics, machine learning)
My research interests center around machine learning and bioinformatics. In particular my current work is focused in two areas: gene regulation and information extraction. I am developing and applying machine learning methods for uncovering the regulatory mechanisms of cells. I am also interested in developing automated methods that enable text sources to be better exploited for discovery and decision making in biomedical domains. Our approach is to use machine learning methods to induce information-extraction routines from training examples. |
Mary Lindstrom (functional data analysis, statistical computing, mixed effects models)
My research focuses on methods for the analysis of sets of curves. This type of data occurs frequently in the biological sciences. For example, we might obtain a dose response curve from each of a number of people (or mice, or cell lines) which fall into two groups (say treatment and control). I work on methods that allow us to compare the groups even if there is no parametric model available (e.g. a polynomial) which fits the curves. |
David DeMets (epidemiologic studies and clinical trials, survival and longitudinal studies)
The theme of my research is in the design, monitoring and analysis of clinical trials, especially the randomized controlled clinical trial. One particular interest is developing statistical methodology for data monitoring and interim analysis. In addition to statistical methodology, issues in the design of clinical trials, such as the role of surrogate outcome measures and multiple outcomes are being considered. |
Ronald Gangnon (spatial data analysis, clinical trials methodology, order restricted methods)
My primary research interest is spatial modeling of disease rates, especially methods for proactively detecting "hot spot" clusters and for reactively assessing the significance of reported clusters. I am currently working to develop models that assess our uncertainty about the location and size of a cluster. Other research interests include order-restricted non-parametric methods for assessing the repeatability of measurements and methods for group sequential monitoring of multiple endpoints in clinical trials.
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Research interests include curriculum development and evaluation, performance-based assessment, survey development, quality of life analysis, and cognitive and neuropsychological assessment, particularly in children with cystic fibrosis and elderly adults with Alzheimer’s Disease
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Michael Newton (computational statistics, statistical methods in molecular biology and genetics, Bayesian inference)
My research considers statistical problems in the biological sciences, especially problems that involve the analysis and interpretation of genetic data. Questions range from how to use molecular sequence data to reconstruct patterns of evolution, to how to characterize genes affecting cancer risk, or to how to infer trends in wild animal abundance. Central to my research is the development and the theoretical analysis of probability models and computational statistical methods. |
Jason Fine (biomedical consulting, cancer clinical trials, quality of life, survival analysis)
My primary research interest is in semi-parametric methods for time-to-event and longitudinal data. I have developed novel semi-parametric models for the cumulative incidence function in the competing risks setting, investigated methods for interim analyses in clinical trials in which reporting delays are present, and studied flexible models for the analysis of quality of life data which arise in medical settings. Another research interest is statistical methods in genetic type research. I also will be developing a course on statistical methods in human genetics. |
An important ecological concept is species diversity. Examples to which this concept can be applied include birds in a forest or bacteria in the stomach. Certain distributions are used to describe the pattern of population size of species and the number of species. Of particular interest are certain indices that are used to summarize diversity. The most common one of these is probably the Shannon Index which is related to entropy ideas. Most ecological studies provide estimates of such diversity indices without consideration of the uncertainty in their estimation and how sampling strategies -- and the underlying species distribution -- affect the estimates. We will explore various ways of quantifying the performance of these diversity indices. |
Marian Fisher (clinical trials, data monitoring, coordinating centers, ophthalmology)
My interests are methods to support data monitoring committees, independent groups that monitor accumulating data for safety and efficacy for ongoing large multi-center clinical trials designed to provide confirmatory evidence to gain FDA approval of a drug. I am particularly interested in graphical techniques for presenting results that allow clinicians to integrate large amounts of comparative information quickly. I have worked on clinical trials in diverse areas such as ophthalmology, cardiovascular disease, trauma, and Lou Gehrig's disease. |
David Page (bioinformatics, machine learning, data mining)
My research is focused on machine learning and data mining with applications to bioinformatics, chemoinformatics, and health sciences. Of particular interest, are techniques such as inductive logic programming that can utilize background knowledge and return human-comprehensible results, and other relational learning techniques capable of dealing with complex data points (such as molecules) and producing logical rules. |
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Associate Professor of Statistics and of Botany |
Assistant Professor Animal Health and Biomedical Sciences
School of Veterinary Medicine |
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Assistant Professor
Department of StatisticsDepartment of Biostatistics and Medical Informatics, |
Professor Departments of Computer Sciences and
Biostatistics & Medical Informatics |
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My research interests emerge from collaboration with biological scientists, particularly through my Biometry appointment at UW-Madison. Recent work has been largely in statistical genetics, with some attention to ecology. Ongoing statistical genetics research involves data analysis, methodology and design; Bayesian inference for QTLs using Markov chain Monte Carlo methods to estimate the joint distribution of QTL number, locations and effects; combining genetic data across multiple experiments and semi-parametric and non-parametric inference for QTLs. Other theoretical and applied genetics research concern mixed models and polyploid genetics. Recent collaborations with Gianola (Animal Science) examines genetic map construction. Considerable recent research has been focused on microarray gene expression data analysis, particularly with Lin (Statistics) and Attie (Biochemistry) on microarray data analysis. Research in ecological modeling builds on novel ideas about individual-based models in population ethology with a software release. |
Marjorie Rosenberg (actuarial methods, health policy and cost issues)
My research interests are in the application of statistical methods to health care, and applying my actuarial expertise to cost and policy issues in health care. I am involved in the development of a cost-effectiveness model to determine whether newborns should be tested for cystic fibrosis. I am also on the faculty and member of the evaluation team for the University of Wisconsin Center of Excellence in Women's Health. Finally, I am interested in the use of insurance claim data in the development of statistical tools that can monitor health care outcome processes. |
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My research is focused on methods to identify genes involved in disease pathogenesis. I have been particularly interested in microarray studies, which measure gene expression for thousands of genes - often an entire genome - simultaneously. With this type of data, the number of measurements of distinct genes across an array greatly exceeds that for any individual gene (large p, small n). This poses a number of interesting statistical problems in experimental design and analysis. |
Ellen Roecker (multi-center clinical trials, interim monitoring)
My research interests are in the application of statistical methods to multi-center clinical trials. Most large multi-center clinical trials have a data and safety monitoring committee to monitor interim data for the purpose of assuring the safety of the participants in the trial, and to allow for the possibility of early termination of the trial for benefit or harm. I am interested in statistical methods used to adjust for the repeated testing of data during interim monitoring, and in the content and format of data monitoring reports. |
KyungMann Kim (clinical trials methodology, ordered categorical data analysis, cancer, ophthalmology, HIV/AIDS)
My research focuses on group sequential methods for data and safety monitoring and early stopping of clinical trials in chronic diseases such as cancer, cardiovascular diseases and human immunodeficiency virus (HIV) diseases and on regression methods for analysis of paired ordered categorical data such as from bilateral eye diseases.
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Grace Wahba (multivariate function estimation, risk factor modeling and estimation, eye diseases)
My research involves multivariate function estimation and machine learning using splines, support vector machines and variational methods. I'm particularly interested in developing models relating risk factors to health outcomes in large medical and environmental studies. I'm also involved in studying risk factors for eye diseases in large demographic studies, where several thousand selected volunteers are followed over a period of time.
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