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Programs and Degrees Admissions & Student Funding |
Biostatistics Research
Analysis of Sample Surveys
Bioinformatics
Data on national health and epidemiology are often collected
using complex probability sample designs involving stratification and
clustering of units. Faculty in Biostatistics are engaged in the design
of such surveys, and in developing methods of analysis. Topics include
designs that combine information from samples and administrative sources,
and robust analysis methods based on models that improve on standard
design-based inferences, and deal with problems of missing data.
Faculty: J. Lepkowski, R. Little, T. Raghunathan
Links: UM Institute for Social Research With the advent of Markov chain Monte Carlo methods in the
late 1990's, Bayesian modeling and analysis has played an ever-increasing
role in the health sciences and public health. Several researchers in
the Department of Biostatistics are contributing to this growth, with
important innovations in automated image analysis, in the analysis of
ordinal and rank data, and in core statistical methodologies like statistical
computing and model assessment. In image analysis, new Bayesian models
for spatial processes enable researchers to match anatomically similar
regions across image data sets. These methods facilitate statistical
analyses of image data across patients. They also promise important
diagnostic benefit through quantitative measurements of disease progression
over longitudinally-matched image data. Ordinal and rank data are common
in public health, and Bayesian methods allow such data collected from
multiple raters to be combined, and permit the study of rater attributes.
Current applications of this methodology include the study of a physician's
ability to assign images consistently to disease classes, and the extent
to which they agree on the thresholds used in class definitions. Methodological
work includes new diagnostics to assess the convergence of numerical
algorithms and tools to assess the adequacy of Bayesian models in describing
population variability.
Faculty: T. Johnson, R. Little, T. Raghunathan,
J. Taylor
Longitudinal and Correlated Data
Correlated data are common in many health sciences studies,
where clustered, hierarchical and spatial data are frequently observed.
A common feature of such data is that observations are correlated and
statistical analysis requires taking such correlation into account.
Examples of clustered data include longitudinal data, familial data,
and analysis of multiple outcomes or recurrent events. Hierarchical data are common in multi-center clinical trials
and community/school-based intervention studies, where correlation is
due to several levels of clustering, such as schools and classes. Spatial
data arise in disease mapping, ecology, environmental health and brain
imaging, where data are correlated due to spatial proximity. Faculty in Biostatistics are engaged in the design and the development
of statistical methodology for such correlated data. Examples of research
areas include random effects models, estimating equations, missing data,
multiple outcomes, nonparametric/semiparametric regression, measurement
error models, and joint modeling survival and longitudinal outcomes.
Faculty: T.
Braun, T. Johnson, J. Kalbfleisch, R. Little, T. Raghunathan,
J. Taylor
Students: H. Zhou Nonparametric and Semiparametric Modeling
In contrast to parametric modeling, where the distribution
of the data is assumed known up to a finite-dimensional parameter, nonparametric
methods involve an infinite dimensional parameter. Nonparametric methods
are widely used in biomedical research. For example, logrank tests and
Kaplan-Meier estimates are standard tools in analyzing censored survival
data. A semiparametric model is intermediate between parametric and
nonparametric models, and contains finite-dimensional and infinite-dimensional
parameters. For example, the widely used Cox model survival data is
semiparametric. Research in semiparametric models has been intense over
the past two decades. In both nonparametric and semiparametric modeling,
empirical methods and smoothing are two major ways to deal with the
infinite-dimensional parameter. Faculty in biostatistics are developing
new methodology and applying nonparametric and semiparametric techniques
in clinical trials, survival analyses, recurrent events, longitudinal
studies, and missing data problems.
Faculty: T. Braun, M. Brown, D. Ghosh, J. Kalbfleisch, R. Little, S. Murray, B. Nan, J. Taylor
Students: A. Andrei, K. Cooper, H. Zhou Statistical Analysis with Missing Data
Empirical studies in the social, behavioral, economic, and
medical sciences frequently suffer from missing data. For instance,
sample surveys often have some individuals who either refuse to participate
or do not supply answers to certain questions, and panel surveys or
longitudinal studies often have incomplete data due to attrition. Simple
approaches to handling the missing data, such as discarding incomplete
cases or filling in estimates of the missing values, often yield biased
or inefficient statistical inferences. Faculty in Biostatistics work
on developing better methods for analyzing missing data, using models
for the data and missing data mechanism, and computational tools such
as the EM algorithm and the Gibbs' sampler.
Faculty: J. Lepkowski, R. Little, J. Kalbfleisch,
T. Raghunathan, J. Taylor
In many medical and scientific studies, investigators are interested in analyzing data on time to an event. Applications of this work arise in areas as diverse as medicine, epidemiology, demography and engineering. In such event history data, interest centers on the timing and occurrence of various kinds of events such as repeated infections, recurrences of disease, or sequences of events that occur through the study period. Further generalizations of these problems include issues of competing risks, complex sampling and censoring mechanisms, and incorporation of time-dependent or longitudinal covariates. The analysis of survival data is an area of great strength in this department. Several of our faculty and students are working in this general area and have made important and fundamental contributions through many research articles, books, and applications. A variety of approaches for the analysis of survival data, including frequentist and Bayesian methods, are being developed at Michigan. Faculty: T. Braun, D. Ghosh, J. Kalbfleisch, S. Murray, B. Nan, D. Schaubel, J. Taylor
Links: UM General Clinical Research Center, UM Center for the Advancement of Clinical Research, UM Comprehensive Cancer Center
Biomedical research, an information-based discipline, is
undergoing a major revolution as novel experimental approaches are yielding
unprecedented amounts of data. Automation and robotics are becoming
integral parts of experimental processes, impacting the way academic
and industrial research is carried out. Experimental biology and medicine are becoming increasingly dependent
on the extensive application of statistics information sciences. Bioinformatics,
the interdisciplinary field at the intersection of life and quantitative
sciences, provides the necessary tools and resources for this endeavor. Modern fundamental and applied research
in the life sciences is critically dependent on this relatively new
discipline. Faculty in the Department of Biostatistics at the University
of Michigan are playing a major role in the development of statistical
methods in bioinformatics. In
collaboration with medical and scientific researchers at the University
of Michigan as well as at other national and international institutions,
faculty are developing procedures for the analysis of data such as single
nucleotide polymorphism (SNP), gene and protein expression data, and
modeling techniques for systems biology.
Faculty: G. Abecasis, M. Boehnke, D. Ghosh, J. Taylor
Students: W. Chen, K. Conneely, M. Li
Links: UM
Bioinformatics, George
Michailidis and Kerby
Shedden (UM Statistics
Faculty), Sharon
Kardia (UM
Epidemiology Faculty), UM
Life Sciences Institute
Recent advances in medical imaging technology allows the
measure of brain activity of the intact, living human brain. Faculty at UM Biostatistics work closely
with researchers throughout the university to study normal brain function
and how diseased patients differ from normals. For example, investigators in UM Psychology
use Functional Magnetic Resonance Imaging (fMRI) to identify brain regions
responsible for working memory, the short term memory used to retain,
for example, a grocery list. Investigators
in UM Psychiatry use Positron Emission Tomography (PET) and fMRI to
study schizophrenic patients, to understand how their reactions to emotionally
provocative images differ from that of normal controls. The statistical
methods applied in this area are computationally intensive and include
Bayesian and massively univariate classical approaches.
Faculty: F. Bookstein, M. Brown, T. Johnson,
R. Little
Links: UM Radiology, UM Cognitive Psychology, UM General Clinical Research Center, UM Comprehensive Cancer Center, UM Geriatics/FB Many faculty and students are actively involved in a broad
spectrum of cancer research projects and in developing statistical methodology
motivated by cancer research. The department has close links with the
University of Michigan Comprehensive Cancer Center. Professor Jeremy
Taylor is director of the Cancer Center Biostatistics Unit and oversees
many of these research activities. Examples of specific projects include
the analysis of gene expression microarray data to profile lung, ovarian,
and prostate cancer; design and analysis of clinical trials to test
new therapeutic agents; analysis of epidemiologic data from a population
based study of African-American men; analysis of animal and human brain
magnetic resonance imaging data to obtain early indications of the response
to chemotherapy; and analysis of biomarkers for the early detection
of cancer. Statistical methodology development is an integral part of
these projects, examples of this are the development of methods for
the analysis of microarray data, developing methods to combine biomarkers,
developing more efficient designs for phase 1 clinical trials, developing
methods for evaluating surrogate endpoints, missing data problems and
developing joint models for longitudinal and survival data.
Faculty: T. Braun, D. Ghosh, T. Johnson, R. Little, S. Murray, B. Nan, J. Taylor
Students: K. Cooper
Links: UM Comprehensive Cancer Center internal site, UM Biostatistical Training in Cancer Research Clinical trial research involves the study of novel therapies in patients with the purpose of identifying the best possible treatment for future use. Our faculty are highly involved in the design, conduct, and analysis of single and multi-center clinical trials in cancer, heart disease, diabetes, hepatitis and pulmonar fibrosis as well as trials in sleep disorders, women's and neonatal health and in the treatment of drug abuse. Our proximity to the excellent University of Michigan Medical School and Comprehensive Cancer Center and the pharmaceutical company, Pfizer, allows high quality learning experiences for graduate students interested in clinical research. Our faculty and students are developing statistical methodologies that identify promising therapies more quickly and less expensively. Other research interests include developing strategies for: reducing or eliminating bias due to informative censoring, gaining information from auxiliary variables, incorporating information about quality of life, group sequential monitoring of trials in non-standard situations, flexibly accounting for measurement error in assessing treatment effects, validating use of surrogate endpoints, conducting cross-over trials subject to censoring and determining the maximum tolerated dose while considering both toxicity and efficacy outcomes. Faculty: T. Braun, M. Brown, D. Ghosh, B. Gillespie, T. Johnson, J. Kalbfleisch, M. Kim, R. Little, S. Murray, B. Nan, J. Taylor Students: A. Andrei, K. Cooper, J. Mumford, H. Zhou Links: UM Medical Center, UM Comprehensive Cancer Center, UM Center for the Advancement of Clinical Research, Pfizer Endocrinology is loosely defined as the study of hormone
secretion and the action of hormones on their target cells. Hormones are secreted by specialized cells and concentration
levels are controlled by complex feedback mechanisms, some of which
are understood and many of which are still a mystery. The secretion of hormones come in many different "flavors". Some are oscillatory in nature, other pulsatile or follow a diurnal
rhythm. Yet others are controlled through a menstrual
or seasonal or developmental rhythm. Concentration levels can be assayed through blood or urine
samples. Investigators
at the University of Michigan are interested in many aspects of normal
and abnormal control of hormone concentration levels and the complex
feedback mechanisms that control these levels. Statistical methods that
have been used to study hormone secretion are time series (classical
and dynamic models), Bayesian statistics, biomathematical models and
non-parametric statistics.
Faculty: M.
Brown, T. Johnson
Students: W. Ye Epidemiology and Public Health
Faculty and students are actively involved in a broad spectrum
of methodological and collaborative research in Epidemiology, Health
Behavior and Health Education, Environmental Health, and Health Policy
and Management. Collaborative
projects with Epidemiology faculty include gene microarray data from
epidemiological studies, studies of social inequality and psychosocial/economic
factors in disease prevention, a national longitudinal study of women
health (SWAN), a cohort study of coronary artery calcification, and
studies of reproduction. Collaborative projects include studies of the
effects of air-pollution and interventions on children with asthma,
school-based intervention studies on children with asthma, intervention
studies on women with heart disease, longitudinal studies on school
dropout and substance abuse, a national drug abuse treatment survey,
and national cost analysis of end-stage renal disease. Many areas of methodological research
have application to problems in public health including case-control
and two stage sampling, survival analysis, disease mapping, group randomization
trials and spatial analysis.
Faculty: M. Boehnke, D. Ghosh, B. Gillespie, R. Little,
B. Nan, T. Raghunathan, J. Taylor
Students: Y. Ye, Y. Zhou Driven by advances in molecular biology and the Human Genome
Project, genetics is taking an ever more central role in the biomedical
sciences. These advances have in turn resulted in an explosive increase
in the quantity and variety of genetic data. Faculty and students at UM Biostatistics are collaborating with
investigators here at the UM Center for Statistical Genetics, elsewhere
across campus, and around the world. Specific studies seek to identify
genes that play a role in human diseases such as diabetes, CANCER, schizophrenia,
macular degeneration, and glaucoma, and that allow discrimination of
different disease or tumor subtypes. Faculty and students also are working
to develop new statistical designs and analytic and computational methods
to help ensure the efficient generation and use of genetic data. We
are involved in the analysis of both traditional genetic data, such
as microsatellite genome scans and genealogical data, as well as high
throughput data from recent technologies, including sequence, SNP and
expression array data. The statistical approaches used in this area
include likelihood-based and Bayesian methods, and often are computationally
intensive.
Faculty: G. Abecasis, M. Boehnke, D. Ghosh, L. Scott, J.
Taylor
Students: K. Conneely, M. Li,
A. Skol
Links: UM Center for Statistical Genetics, UM Genome Science Training Program, UM Biostatistical Training in Cancer Research, UM Comprehensive Cancer Center, UM Department of Human Genetics,UM Public Health Genetics Interdepartmental Concentration Organ Failure and Transplantation
There are more than 300,000 people alive today in the United States because they are receiving ongoing replacement treatment for their failed kidneys, hearts, livers, or other organs. Dialysis is the most common treatment for kidney failure while transplantation is a common treatment for liver, heart, and also kidney failure. Data are available for nearly all patients for many aspects of these diseases due to the availability of federal health insurance for kidney failure patients in the U.S. and the use of a national organ sharing system for all transplanted organs in the U.S. These data systems track many aspects of patient condition, treatment methods, outcomes, and costs through the course of these diseases. Several national studies of organ failure are based at the University of Michigan with collaborative work carried out by the Departments of Biostatistics, Internal Medicine, Surgery, Epidemiology, and Health Management and Planning. The study of population based data, rather than controlled experimental outcomes, requires careful attention to research design and control of bias. Many opportunities exist for collaborative efforts to develop statistical methods to deal with these issues in longitudinal data analyses. Faculty: B. Gillespie, S. Murray, D. Schaubel, J. Kalbfleisch.
Link: UM Kidney Epidemiology and Cost Center
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Research Centers affiliated with the Biostatistics Department BORC: Biometrics and Outcomes Research Core. KECC: Kidney Epidemiology and Cost Center. CSG: Center for Statistical Genetics. MICHR: Michigan Institute for Clinical and Health Research. BCMRC: Biostatistics Collaborative and Methodoly Research Core. Stat Core: Cancer Center Statistical Core. CTSA: Clinical and Translational Science Awards. ______________
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Printed from http://www.sph.umich.edu/biostat/research.html on May 18, 2008
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