Yi Li

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Yi Li is a Professor of Biostatistics and Director of the Kidney Epidemiology and Cost Center. His current research interests are survival analysis, longitudinal and correlated data analysis, measurement error problems, spatial models and clinical trial designs. He is developing methodologies for analyzing large-scale andhigh-dimensional datasets, with direct applications inobservational studies as well in genetics/genomics. His methodologic research is funded by various federal grants starting from year 2003. Yi Li is actively involved in collaborative research in clinical trials and observational studies with researchers from the University of Michigan and Harvard University. The applications have included chronic kidney disease surveillance, organ transplantation, cancer preventive studies and cancer genomics.

Matthew Schipper

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Matthew Schipper, PhD, is Professor in the Departments of Radiation Oncology and Biostatistics. He received his Ph.D. in Biostatistics from the University of Michigan in 2006. Prior to joining the Radiation Oncology department he was a Research Investigator in the Department of Radiology at the University of Michigan and a consulting statistician at Innovative Analytics.

Prof. Schipper’s research interests include:

  • Use of Biomarkers to Individualize Treatment – Selection of dose for cancer patients treated with Radiation Therapy (RT) must balance the increased efficacy with the increased toxicity associated with higher dose. Historically, a single dose has been selected for a population of patients (e.g. all stage III NSC lung cancer). However, the availability of new biologic markers for toxicity and efficacy allow the possibility of selecting a more personalized dose. I am interested in using statistical models for toxicity and efficacy as a function of RT dose and biomarkers to select an optimal dose for an individual patient. We are studying quantitative methods based on utilities to make this efficacy/toxicity tradeoff explicit and quantitative when biomarkers for one or multiple outcomes are available. We have proposed a simulation based method for studying the likely effects of any model or marker based dose selection on both toxicity and efficacy outcomes for a population of patients. In related projects, we are studying the role of correlation between the sensitivity of a patient’ tumor and normal tissues to radiation. We are also studying how to utilize these techniques in combination with baseline and/or mid-treatment adaptive image guided RT.
  • Early Phase Oncology Study Design – An increasingly common feature of phase I designs is the inclusion of 1 or more dose expansion cohorts (DECs) in which the MTD is first estimated using a 3+3 or other Phase I design and then a fixed number (often 10-20 in 1-10 cohorts) of patients are treated at the dose initially estimated to be the MTD. Such an approach has not been studied statistically or compared to alternative designs. We have shown that a CRM design, in which the dose-assignment mechanism is kept active for all patients, more accurately identifies the MTD and protects the safety of trial patients than a similarly sized DEC trial. It also meets the objective of treating 15 or more patients at the final estimated MTD.  A follow-up paper evaluating the role of DECs with a focus on efficacy estimation is in press at Annals of Oncology.

Michael Boehnke

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My research focuses on developing statistical methods and software tools for the analysis of human genetic data and application of those methods to understand the genetic basis of human health and disease. Our methods and tools are used by statisticians and geneticists worldwide. My disease research is focused on type 2 diabetes (T2D) and related traits and on bipolar disorder and schizophrenia. Our studies are generating and analyzing genome or exome sequence data on 10,000s of individuals, requiring the efficient handling of petabyte-scale data.

Michael Elliott

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Michael Elliott is Professor of Biostatistics at the University of Michigan School of Public Health and Research Scientist at the Institute for Social Research. Dr. Elliott’s statistical research interests focus around the broad topic of “missing data,” including the design and analysis of sample surveys, casual and counterfactual inference, and latent variable models. He has worked closely with collaborators in injury research, pediatrics, women’s health, and the social determinants of physical and mental health. Dr. Elliott serves as an Associate Editor for the Journal of the American Statistical Association. He is currently serving as a co-investigator on the MIDAS-affiliated Reinventing Urban Transportation and Mobility project, working to develop methods to improve the representativeness of naturalistic driving data.

Jeremy M G Taylor

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Jeremy Taylor, PhD, is the Pharmacia Research Professor of Biostatistics in the School of Public Health and Professor in the Department of Radiation Oncology in the School of Medicine at the University of Michigan, Ann Arbor. He is the director of the University of Michigan Cancer Center Biostatistics Unit and director of the Cancer/Biostatistics training program. He received his B.A. in Mathematics from Cambridge University and his Ph.D. in Statistics from UC Berkeley. He was on the faculty at UCLA from 1983 to 1998, when he moved to the University of Michigan. He has had visiting positions at the Medical Research Council, Cambridge, England; the University of Adelaide; INSERM, Bordeaux and CSIRO, Sydney, Australia. He is a previously winner of the Mortimer Spiegelman Award from the American Public Health Association and the Michael Fry Award from the Radiation Research Society. He has worked in various areas of Statistics and Biostatistics, including Box-Cox transformations, longitudinal and survival analysis, cure models, missing data, smoothing methods, clinical trial design, surrogate and auxiliary variables. He has been heavily involved in collaborations in the areas of radiation oncology, cancer research and bioinformatics.

I have broad interests and expertise in developing statistical methodology and applying it in biomedical research, particularly in cancer research. I have undertaken research  in power transformations, longitudinal modeling, survival analysis particularly cure models, missing data methods, causal inference and in modeling radiation oncology related data.  Recent interests, specifically related to cancer, are in statistical methods for genomic data, statistical methods for evaluating cancer biomarkers, surrogate endpoints, phase I trial design, statistical methods for personalized medicine and prognostic and predictive model validation.  I strive to develop principled methods that will lead to valid interpretations of the complex data that is collected in biomedical research.

Ananda Sen

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Dr. Sen is the Lee A Green Collegiate Research Professor at the University of Michigan. He holds research professor titles in the departments of Family Medicine and Biostatistics. He is a fellow of the American Statistical Association and an elected member of the International Statistical Institute. He has an established career record as a statistician with over 140 publications in peer-reviewed journals and invited book chapters. He has methodological expertise in the areas of competing risks, analysis of recurrent event data, and measuring inter-rater agreement. Dr. Sen has extensive collaborative experience in cancer prevention and biomarker research with diet and disease endpoints. He is the principal investigator of the Biostatistics Core in the NCI funded Specialized Program on Research Excellence in GI Cancer at the University of Michigan. He serves as the Book Review Editor for the journal International Statistical Review. He is on the editorial board of the journals Communications in Statistics, and Computational Statistics and Data Analysis, and serves as a statistics co-editor for the American Journal of Preventive Medicine. In the past, he has also served as an expert panelist for the Committee on National Statistics (CNSTAT), in the National Academy of Sciences.

Kerby Shedden

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Kerby Shedden has broad interests involving applied statistics, data science and computing with data.  Through his work directing the data science consulting service he has worked in a wide variety of application domains including numerous areas within health science, social science, and transportation research.  A current major focus is development of software tools that exploit high performance computing infrastructure for statistical analysis of health records, and sensor data from vehicles and road networks.

Peter X. K. Song

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Dr. Song interested in the development and application of theories and methodologies from Data Science to solve scientific problems arising from medical and public health sciences, in particular from the fields of environmental health sciences and nutritional sciences. People from his lab are strongly interested in interdisciplinary research in the areas of statistics, operation research, and machine learning, with the core interest in the statistical foundation of big data analytics, and with target applications in processing and analyzing big data from various applied sciences, including asthma, environmental health sciences, nephrology, and nutritional sciences. His research projects have been funded by NIH, NSF and DARPA funding agencies. Visit Song Lab webpage for detail: http://www.umich.edu/~songlab/

Jack D. Kalbfleisch

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Dr. Kalbfleisch is a Professor of Biostatistics and Statistics at the University of Michigan, Ann Arbor. He served as chair of the Department of Biostatistics, School of Public Health, from 2002 to 2007 and as Director of the Kidney Epidemiology and Cost Center from 2008 to 2011. He received his Ph.D. in statistics in 1969 from the University of Waterloo. He was an assistant professor of statistics at the State University of New York at Buffalo (1970-73) and on faculty at the University of Waterloo (1973-2002). At Waterloo, he served as chair of the Department of Statistics and Actuarial Science (1984-1990) and as dean of the faculty of Mathematics (1990-1998). He has held visiting appointments as Professor at the University of Washington, the University of California at San Francisco, the University of Auckland, Fred Hutchinson Cancer Research Center and the National University of Singapore. He has interests in and has publised in various areas of statistics and biostatistics including life history and survival analysis, likelihood methods of inference, bootstrapping and estimating equations, mixture and mixed effects models and medical applications, particularly in the area of renal disease and organ transplantation. Dr. Kalbfleisch is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. He is also an elected member of the International Statistical Institute, a Fellow of the Royal Society of Canada and a Gold Medalist of the Statistical Society of Canada. He also received the Distinguished Research Award from the UM School of Public Health in 2011.

A primary research interest is in the development of models and methods for analyzing failure time or event history data. Applications of this work arise in many areas including epidemiology, medicine, demography and engineering. In event history data, interest centers on the timing and occurrence of various kinds of events such as, for example, repeated infections or recurrences of disease, or other sequences of events that may occur during a study period. I have been particularly interested in situations in which only partial data or data subject to sampling bias are available.

In recent years, I have been working on statistical aspects of problems associated with End Stage Renal Disease and solid organ transplantation. The Kidney Epidemiology and Cost Center has many projects associated with these including various projects funded through the Centers for Medicare and Medicaid Services. This  provides a rich area of application where statistical methods and developments play a substantial role in defining public policy. I am particularly interested in the development of appropriate methods for the use of such data in profiling and/or ranking medical providers.  

I have recently worked on the optimization and simulation of kidney paired donation programs. In these, candidates in need of a kidney transplant who have a willing but incompatible living donor are entered into a pool and we seek exchanges of donors to overcome incompatibilities. Added to this is the potential for non-directed donors who can give a kidney to one member of the pool and hence create a chain of transplants. Our methods use integer programming methods to create flexible allocation schemes that have the potential to provide substantial increases in the number of transplants achieved.

Roderick Little

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Roderick Joseph Little, PhD, is the Richard D. Remington Distinguished University Professor of Biostatistics, Professor of Statistics, Research Professor, Institute for Social Research, and Senior Fellow, Michigan Society of Fellows, at the University of Michigan, Ann Arbor.

Prof. Little’s primary research interest is the analysis of data sets with missing values. Many statistical techniques are designed for complete, rectangular data sets, but in practice biostatistical data sets contain missing values, either by design or accident. As detailed in my book with Rubin, initial statistical approaches were relatively ad-hoc, such as discarding incomplete cases or substituting means, but modern methods are increasingly based on models for the data and missing-data mechanism, using likelihood-based inferential techniques.

Another interest is the analysis of data collected by complex sampling designs involving stratification and clustering of units. Since working as a statistician for the World Fertility Survey, I have been interested in the development of model-based methods for survey analysis that are robust to misspecification, reasonably efficient, and capable of implementation in applied settings. Statistics is philosophically fascinating and diverse in application. My inferential philosophy is model-based and Bayesian, although the effects of model misspecification need careful attention. My applied interests are broad, including mental health, demography, environmental statistics, biology, economics and the social sciences as well as biostatistics.