Biostatistics PhD Program

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MESSAGE FROM THE DIRECTOR

Kwang Woo Ahn, PhD

 

Kwang Woo Ahn, PhD

Associate Professor of Biostatistics
Director, Graduate Program in Biostatistics

 

“Thank you for your interest in Division of Biostatistics at MCW. In modern clinical and basic science, investigators face challenges in the design of experiments, data collection, data analysis, and interpretation of data. Division of Biostatistics at MCW trains students to meet/solve such challenges. Our division has ideal research environments for Biostatistics through numerous collaborations with Center for International Blood and Marrow Transplant Research (CIBMTR), Center for Patient Care and Outcomes Research (PCOR), Human and Molecular Genetics Center, among others. Many of our methodological works are motivated by these collaborations. All of our PhD students successfully found a position in industry and academia upon graduation. Our division is one of the fast growing divisions at MCW. However, we still keep family-oriented atmosphere that all of our division members truly appreciate. We welcome applications from students who want to be prepared as a balanced biostatistician in theory and application with our family. If you have any questions on our program, please do not hesitate to contact me.”

kwooahn@mcw.edu
(414) 955-7387

Our Biostatistics PhD program is a highly collaborative unit that is integral in the design of numerous research projects within MCW and its affiliates, which include the MCW Cancer Center, Center for International Blood and Marrow Transplant Research and The Center for Patient Care and Outcomes Research. In this program, you will receive in-depth training on the use of state-of-the-art software and consulting opportunities. Additionally, you will benefit from an expansive network of faculty, both during your tenure and as you seek a career upon completing your course of study.

  • About
  • Students
  • Curriculum
  • Admissions
  • Tuition & Fees
  • Faculty
  • Alumni

IDP Graduate Program in Microbiology and ImmunologyAbout

 

The Division of Biostatistics offers a PhD degree program designed for students with strong undergraduate preparation in mathematics and trains students in biostatistical methodology, theory, and practice.

Emphasis is placed on sound theoretical understanding of statistical principles, research in the development of applied methodology, and collaborative research with biomedical scientists and clinicians. In addition, students gain substantial training and experience in statistical computing and in the use of software packages.

Courses in the program are offered in collaboration with the Department of Mathematics at the University of Wisconsin–Milwaukee, with several required courses taught on the UWM campus. Students can also take courses at Marquette University. The degree requirements, including dissertation research, are typically completed in five years beyond a bachelor’s degree that includes strong mathematical preparation.

Class sizes are small. Usually student to faculty ratios are better than 1:1.

Career Possibilities after Graduation:

  • Pharmaceutical & Consultant Industries
  • Government & Non-Profit Agencies
  • Academic Institutions

Specifically, some Alumni have been employed at MCW, Wake Forest University, SAS Business Analytics and Business Intelligence Software, Novartis, and Takeda Pharmaceuticals following graduation.

 

 

Nicholas DeVogelNicholas DeVogel

ndevogel@mcw.edu
Mentor/Advisor: Tao Wang, PhD
Year Entered MCW: 2014
Previous Education: BA in Honors Statistics and Mathematics at UW Madison
Research Interest: Statistical genetics

Yizeng HeYizeng He

yizhe@mcw.edu
Mentor: Kwang Woo Ahn, PhD
Year in Program: 2016
Previous Education: BA in Statistics and Mathematics at UW Madison
Research Interest: Survival analysis/Personalized medicine

Manoj KhanalManoj Khanal

mkhanal@mcw.edu
Mentor: Kwang Woo Ahn, PhD
Year in Program: 2018
Previous Education: MS in Mathematics at University of North Dakota from 2013 to 2015 & PhD student in Mathematics at University of Wisconsin Milwaukee from August 2015 to December 2017 (transferred from UWM to MCW).
Research Interest: Biostatistics

Tucker KeuterTucker Keuter

tkeuter@mcw.edu
Mentor: Anjishnu Banerjee, PhD
Year in Program: 2014
Previous Education: BS in Mathematics and Economics at Gonzaga University
Research Interest: Bayesian analysis, spatial analysis, machine learning

Xiao LiXiaojie Liu

xiaoli@mcw.edu
Mentor: Kwang Woo Ahn, PhD
Year in Program: 2017
Previous Education: MS in Biostatistics at Georgetown University
Research Interest: Biostatistics

Xinran QiXinran Qi

xinqi@mcw.edu
Mentor: Kwang Woo Ahn, PhD
Year in Program: 2016
Previous Education: BS in Chemistry and Statistics at Sun Yat-sen University
Research Interest: Biostatistics

Natasha SahrNatasha Sahr

nsahr@mcw.edu
Mentor: Kwang Woo Ahn, PhD
Year in Program: 2013
Previous Education: BS in Mathematics at Marquette University
Research Interest: Variable selection/Survival analysis

Charles SpanbauerCharles Spanbauer

cspanbauer@mcw.edu
Mentor: Kwang Woo Ahn, PhD
Year in Program: 2015
Previous Education: MS in Biostatistics at University of Illinois at Chicago.  
Research Interest: BART/Bayesian analysis

Bonifride TuyishimireBonifride Tuyishimire

btuyishimire@mcw.edu
Mentor: Prakash Laud, PhD and Brent Logan, PhD
Year in Program: 2013
Previous Education: BS in Mathematics at Henderson State University
Research Interest: Variable selection/Survival analysis

Bonifride TuyishimireYayun Xu

yayunxu@mcw.edu
Mentor: Mei-Jie Zhang, PhD
Year in Program: 2015
Previous Education: MS in Statistics at University of Connecticut
Research Interest: Survival and Competing risks data analysis

Curriculum

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  Sample Program Plan

Typical sequence for the completion of required courses (starting in even year)

Fall 1:
04214: Clinical Trials
04224: Biostat Computing
04231: Models & Methods I
04261: Statistical Inference I
Elective/Math/Programming
 

Spring 1:
04232: Models & Methods II
04262: Statistical Inference II
04285: Intro. Bayesian
04221: Theory of Consulting

Summer 1:
04222: Consulting
04295: Readings & Research
Elective/Prob/Programming

Fall 2:
04385: Advanced Bayesian 
04233: Models & Methods III
04313: Advanced Computing
Epi Elective Course

Spring 2:
04275: Applied Survival 
04363: Advanced Statistics I
Bioinformatics

Summer 2:
04295: Readings & Research
Consulting/Elective

Fall 3:
04275: Linear Models I
04386: Advanced Survival 
Elective Course

Spring 3:
04384: Statistical Genetics
Elective Course

 


Typical sequence for the completion of required courses (starting in odd year)

Fall 1:
04231: Models & Methods I
04224: Biostat Computing
04261: Statistical Inference I
Elective/Math/Programming/Epi

Spring 1:
04232: Models & Methods II
04262: Statistical Inference II
04275: Applied Survival 
04221: Theory of Consulting

Summer 1:
04222: Consulting
04295: Readings & Research
Elective/Prob/Programming

Fall 2:
04214: Clinical Trials
04386: Advanced Survival 
04275: Linear Models I 
 

Spring 2:
04285: Intro. Bayesian 
04384: Statistical Genetics
Bioinformatics
 

Summer 2:
04295: Readings & Research
Consulting/Elective

Fall 3:
04264: Adv. Bayesian 
04233: Models & Methods III
04313: Adv. Statistical Computing 
Elective Course

Spring 3:
04363: Adv. Statistics I
Elective Course

 

** Italicized courses are taught at the UWM Mathematics Department.

 

Graduation Requirements

 

A minimum of 6 credit hours of biological/medical science electives are required, and may be selected from the list below. Students may also take appropriate courses from UWM to satisfy the elective requirements. Electives must be approved by the advisory committee.

  Preliminary Examination

Upon completion of the necessary courses (Statistical Models & Methods I, II, III and Statistical Inference I, II) the student will be given a written preliminary examination, usually in January of the second academic year. This examination will be organized and administered by the graduate studies committee. The exam will consist of two parts - Applied Statistics and Theory of Statistics. The applied part will cover Statistical Models and Methods I, II and III, Clinical Trials, and Biostatistical Computing and Data Management and possibly Applied Survival Analysis or Applied Bayesian Analysis. The theory part will cover the materials from Statistical Inference I & II. This will be a standard divisional exam, and evaluation will be done by the whole faculty. The criteria for evaluation will be based on student's understanding and competency in basic principles and foundations of biostatistics, and his/her potential for conducting independent research in statistical methods and applications. If a student does not pass this exam, he/she will have a second opportunity to take it. The preliminary examination will be offered every January and August by the Division. The student must pass this examination to continue in the PhD program.

  Readings & Research

The student is required to take BIOST 295 Readings & Research for 3 credit hours each with two different members of the faculty. Typically this is done in the first two summers and in the process of selecting a dissertation topic and advisor.

  Qualifying Examination

Upon successful completion of the preliminary exam and the required biostatistics courses (usually at the end of the third year), the student will be given a qualifying examination. This examination is tailor-made for each student, and it is organized, administered and evaluated by his/her advisory committee. The evaluations will be based on student's in-depth understanding and competency in advanced topics in biostatistics, and his/her ability and maturity to apply the knowledge earned from the course-work in doing meaningful research. The exam consists of two parts. The first part will be an oral examination testing the student's general statistical knowledge at the advanced level. The second part consists of writing a dissertation proposal and presenting it to the division. This proposal must be approved by his/her advisory committee. A student not passing either part of the exam may be given another chance to retake that part within three months of the first attempt. Students passing this exam will be admitted to PhD candidacy.

  Final Examination

The PhD candidate must submit a dissertation representing an original research contribution. It must show high attainment and clear ability to carry out independent biostatistics research of publishable quality. The final oral examination will be administered by his/her advisory committee after the student has completed all other formal requirements for the PhD degree. It will be a public defense of the dissertation. The student also will be expected to demonstrate a good understanding of materials relevant to the general field in which the dissertation is written. The student's advisory committee will evaluate the performance of the student in the dissertation defense.

  Dissertation Research Requirements

The student begins his/her dissertation research during the third year. The initial step consists of identifying a topic that is of mutual interest to the student and a member of the faculty who serves as the dissertation advisor. Courses, talks and presentations by the faculty assist the student in this process. After a literature survey and a clearer definition of the scope of the research under the direction of the advisor, the student submits a written proposal and presents it orally to the advisory committee. During the conduct of the dissertation research the advisory committee meets periodically to monitor the student's progress. Upon completion of the proposed research the student submits the dissertation and defends it in a public presentation.

 

The dissertation must be an original contribution to scientific knowledge. It can involve development of new statistical methodologies, evaluation of existing methodologies and study of their properties, innovative application of existing methodologies, or any combination of the above. The dissertation should be of publishable quality in peer reviewed journals in biostatistics or statistics.

  Required Courses

BIOETH 10222a – Ethics and Integrity in Science (1 credit)

BIOST 04214 – Design and Analysis of Clinical Trials (3 credits)

BIOST 04220 – Research Seminar (1 credit)

BIOST 04221 – Theory of Consulting (2 credits)

BIOST 04222 – Statistical Consulting (3 credits)

BIOST 04224 – Biostatistical Computing (3 credits)

BIOST 04231 – Statistical Models and Methods I (3 credits)

BIOST 04232 – Statistical Models and Methods II (3 credits)

BIOST 04233 – Statistical Models and Methods III (3 credits)

BIOST 04275 – Applied Survival Analysis (3 credits)

BIOST 04285 – Introduction to Bayesian Analysis (3 credits)

BIOST 04295 – Reading and Research (1-9 credits)

BIOST 04313 – Statistical Computing (3 credits)

BIOST 04363 – Advanced Statistics I (3 credits)

BIOST 04365 – Linear Models I (3 credits)

BIOST 04384 – Statistical Genetics (3 credits)

BIOST 04385 – Advanced Bayesian Analysis (3 credits)

BIOST 04386 – Advanced Survival Analysis (3 credits)

BIOST 04399 – Doctoral Dissertation Hours (1-9 credits)

BIOST 04231/MTHSTAT 761* - Mathematical Statistics I (3 credits)

BIOST 04232/MTHSTAT 761* - Mathematical Statistics II (3 credits)

BIOST PH 721* Introduction to Translational Bioinformatics (3 credits)

 

* UW-Milwaukee course

  Elective Courses

BIOETH 201 – Medical Ethics (2 credits)

BIOETH 222 – Ethics and Integrity in Science (2 credits)

BIOETH 232 – Ethics, Policy and Genetic Technology (2 credits)

BIOPHYSICS 215 – Medical Physics (1 credit)

CELLBIO 150 – Introduction to Cell Biology (1 credit)

CELLBIO 152 – Human Development (1 credit)

CELLBIO 207 – Introduction to Neuroscience (2 credits)

EPI 201 – Clinical Epidemiology (3 credits)

EPI 256 – Research Methods in Epidemiology (3 credits)

EPI 272 – Epidemiology of Cardiovascular Disease (1 credit)

EPI 274 – Cancer Epidemiology

PHARM 202 – Survey of Pharmacology (3 credits)

PHY 202 – General Human Physiology (6 credits)

PHY 285 – Mathematical Biology (3 credits)

Admissions

Start Your Application Now

Applicants to the Biostatistics PhD program will have ideally have…

  • Completed an undergraduate degree in mathematics or closely related field
  • Completed courses in advanced calculus, matrix/linear algebra and scientific programming with a minimum grade of B in each. Those who have not done so may be considered for admission and, if admitted, must complete these requirements during the first year of study.
  • A strong interest in Biostatistics and biomedical applications
  • An overall grade point average of 3.0 or better
  • A 3.0 grade point average or better in mathematics and science
  • Scores in the 50th percentile or higher on the Quantitative and Verbal components, and a 3.5 or greater on the Analytical Writing component of the Graduate Record Examination (GRE) is ideal. Our Institution Code is 1519.
  • Applicants who studied overseas or via an online U.S.-based institution are required to take a Test of English as a Foreign Language (TOEFL) and make arrangement for an official score report to be sent directly from ETS to the Graduate School of Biomedical Sciences. A TOEFL score is 100 or higher is ideal. Our Institution Code is 1519.

Each year we will select 2-3 highly qualified students interested in furthering knowledge and research skills in Biostatistics.

How To Apply

The MCW Graduate School operates on a rolling admissions basis. However, applications accepted by the priority application deadline of January 15th will receive first priority for admission the following Fall. Students are admitted once per year. Part time students may be admitted in any semester. However, financial support from the Medical College of Wisconsin is not available for part time students.

TUITION AND FEES INFORMATION

If you have questions regarding tuition or your account, please contact the Office of Student Accounts, at (414) 955-8172 or mcwtuition@mcw.edu.

PhD Students

All full-time PhD students receive a full tuition remission, health insurance and stipend.

2017-2018 Stipend: $29,136.00

The following fees are covered by the tuition remission:

  • All Students Fee (Fall & Spring Semesters Only): $40.00
  • Graduate Student Association (GSA) Fee: $35.00
  • Tuition Per Credit - Fall, Spring & Summer Semesters: $1,250.00

Masters, Certificate & Non-Degree Students

Students seeking financial aid for MPH, MS or MA degree programs, visit the Financial Aid Office website.

  • Tuition Per Credit - Fall, Spring & Summer Semesters: $1,010.00
  • Master’s in Medical Physiology Tuition: $42,000/year
  • Continuation: $225.00
  • Audit - Per Class: $100.00

Current MCW Employees

Tuition Course Approval Form - Human Resources (PDF)

Late Fees

There will be a $250 late registration fee for anyone not completing registration by the date indicated on the schedule each semester. There is also a $250 late payment fee for tuition not paid on time according to the Tuition Payments policy in the Student Handbook.

Late payment fee is in addition to any late registration fee.

FACULTY

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  Methodological Research

Survival Analysis

The Division is developing an international reputation as a locale for statistical research in the area of survival analysis and longitudinal data analysis. This area is anchored by the collaborative work of Mei-Jie Zhang, PhD with the researchers at Department of Biostatistics at the University of Copenhagen. Other faculty that have contributed to this area include: Brent Logan, PhD, Kwang Woo Ahn, PhD, Ruta Brazauskas, PhD, Rodney Sparapani, PhD, Soyoung Kim, PhD, and Noorie Hyun, PhD.

 

Clinical Trials

Brent Logan, PhD, working in the area of multiple comparisons, has interests in clinical trials with multiple end points and multiple decisions. He has proposed designs for randomized phase II clinical trials in which one is interested in evaluating several new treatments prior to a comparative phase III clinical trial. Professor Logan provides biostatistical support to a multicenter clinical trials network for blood and marrow transplant research.

 

Bayesian Methodology

Bayesian methodology is finding increasing number of applications in Biostatistics. This is reflected in the work of the faculty in the Division. Prakash Laud, PhD has been conducting methodological research in Bayesian nonparametric and semiparametric techniques, especially as applicable to survival data. He has also published papers in parametric Bayesian methodology, in linear and generalized linear models with an emphasis on model selection. Faculty members Brent Logan, PhD and Rodney Sparapani, PhD have been working on BART (Bayesian Additive Regression Trees) with applications to survival analysis and personalized medicine. Another faculty member, Anjishnu Banerjee, PhD, has been publishing papers on nonparametric methods. 

 

Multiple Comparisons

The problem of multiple comparisons arises when performing many hypothesis tests, in which case the “familywise error rate” or chance of one or more incorrect significant findings among all these tests increases with the number of tests being performed. Brent Logan, PhD has a keen interest in this area in which he continues to publish new methods and applications. Work with Daniel Rowe, PhD, has resulted in investigating multiple comparison thresholding techniques in single-subject fMRI analysis. Work with Ajit Tamhane, PhD, at Northwestern University includes the problem of comparing two treatments at multiple endpoints and multiple comparison procedures to identify the minimum effective dose and/or maximum safe dose in the dose-response setting. Collaboration with Mei-Jie Zhang, PhD, has investigated methods for controlling the familywise error rate when performing pairwise comparisons among several groups when the outcome is the time to an event of interest.

 

Statistical Genetics and Bioinformatics

The Division's research in statistical genetics is led by Tao Wang, PhD and Ying Liu, PhD, with recent work includes various genetic data analyses, linkage and association mapping of disease genes, modeling and methodology development for association mapping of quantitative trait loci, and haplotype association analysis of single nucleotide polymorphisms (SNPs). Specifically, Tao Wang, PhD, explored the definitions and properties of additive, dominance and epistatic effects of QTL and partition of genetic variance in an equilibrium as well as in a disequilibrium population. Additional work resulted in the development of a population-based multipoint LD method for fine mapping of quantitative trait loci. A mixture model was applied to describe the relationship between phenotype and QTL genotypes. An EM algorithm was developed to estimate the genetic effects of QTL and joint haplotype frequencies of QTL and markers. Ying Lui, PhD, is interested in developing methodology in statistical genetics, translational bioinformatics, microbiome, power and sample size calculation tool for NGS data, integrative/meta-analysis methods for different omics data, and supervised/unsupervised machine learning applications. For research interests in power and sample size calculation tool, he has focused on two applications in RNA-Seq and Methyl-Seq data. He has worked closely with collaborators from different fields, including cardiovascular epidemiology, psychiatry, and cancer biology. He has experiences in various types of omics data, including single nucleotide polymorphism (SNP), copy number variation (CNV), DNA methylation, gene expression, proteomics (peptide), and metabolomics data.

 

Variable Selection

Variable selection is one of the central issues in the application of statistical methods. Faculty members Kwang Woo Ahn, PhD and Soyoung Kim, PhD have been working on variable selection for survival and competing risks models with possibly high dimensional data.

 

Personalized Medicine

Personalized medicine has become an important research area in biostatistics. The main goal is developing a statistical tool to identify the optimal treatment given each patient’s characteristics. Faculty members Prakash Laud, PhD, Brent Logan, PhD, and Rodney Sparapani, PhD have been working on BART (Bayesian Additive Regression Trees) with applications to survival analysis and personalized medicine. Another faculty member, Ying Liu, PhD uses machine learning to develop new statistical techniques to find the optimal individualized treatment regime. Relatedly, Kwang Woo Ahn, PhD and Soyoung Kim, PhD have been working on this area using the propensity score for survival and competing risks outcomes.

  Publications/Presentations
  Collaborative Research

There are many research organizations within the College and its affiliates with whom the Division's faculty conduct joint research. Via Research Assistantships and Consulting Services students get an opportunity to work in collaborative projects with groups such as those below.

 

Biostatistics Consulting Service

The Medical College of Wisconsin Biostatistics Consulting Service provides statistical support to biomedical investigators.

 

BMTCTN

The Blood and Marrow Transplant Clinical Trials Network (BMTCTN) is a multi-center network funded by the NIH and NCI to implement clinical trials in the field of hematopoietic stem cell transplantation.

 

CIBMTR

The Center for International Blood and Marrow Transplant Research (CIBMTR) was formed ten years ago by a merger of the research efforts of the National Marrow Donor Program in Minneapolis and the International Blood and Marrow Transplant Registry (IBMTR) at the Medical College of Wisconsin.

 

Molecular Genetics

The Genomic Sciences and Precision Medicine Center at the Medical College of Wisconsin provides academic support for researchers at MCW who use the genomic sequence to understand disease and translate this information from the laboratory to the patient.

 

NMDP

The National Marrow Donor Program (NMDP) is the primary source of potential donors for patients seeking and unrelated hematopoietic stem cell transplantation.

 

PCOR

The Center for Patient Care and Outcomes Research (PCOR) at the Medical College of Wisconsin is made up of faculty and support staff who focus on research related to health care services and patient outcomes

  Technical Reports

Tech Report #66 August 2017 (PDF)
Evaluating Performance of Survival Regression Models with Interval Censored Data in Motor vehicle Crash Experiments
By: Anjishnu Banerjee, Narayan Yoganandan, Fang-Chi Hsu, Scott Gayzik, Frank Pintar

 

Tech Report #65 June 2017 (PDF)
Low Information Omnibus (LIO) Priors for Dirichlet Process Mixture Models
By: Yushu Shi, Michael Martens, Anjishnu Banerjee, Prakash Laud

 

Tech Report #64 April 2017 (PDF) data file (tar)
Nonparametric recurrent events analysis with BART and an application to the hospital admissions of patients with diabetes
By: Rodney Sparapani, Lisa Rein, Sergey Tarima, Tourette Jackson, John Meurer

 

Tech Report #63 March 2016 (PDF)
Accurately Approximating the log gamma distribution with a convolution of normals
By: Prakash Laud, Rodney Sparapani, Brent Logan

 

Tech Report #62 December 2014 (PDF)
Quantile residual lifetime analysis for dependent survival and competing risks data
By: kwang Woo Ahn, Brent Logan

Tech Report #61 December 2013 (PDF)
A proportional hazards regression model for the subdistribution with covariates adjusted censoring weight for competing risks data
By: Peng He, Tomas H. Scheike, Mei-Jie Zhang

 

Tech Report #60 December 2013 (PDF)
An approach for modeling cross-immunity of two strains, with application to variants of Bartonella in terms of genetic similarity
By: Kwang Woo Ahn, Michael Kosoy, Kung-Sik Chan

 

Tech Report #59 May 2013 (PDF)
Interim sample size recalculation for linear and logistic regression models: A comprehensive Monte-Carlo study
By: Sergey Tarima, Peng He, Tao Wang, Aniko Szabo

 

Tech Report #58 April 2012 (PDF)
Design Resampling for Interim Sample Size Recalculation
By: Sergey Tarima, Peng He, Tao Wang, Aniko Szabo

Tech Report #57 April 2012 (PDF)
Internal Pilot Studies: An Annotated Bibliography
By: Aniko Szabo, Tao Wang, Peng He, and Sergey Tarima

Tech Report #56 March 2012 (PDF)
Analysis of Paired and Clustered Time-to-Event Data: An Annotated Bibliography
By: Jennifer Le-Rademacher, John P. Klein, Ruta Brazauskas, and Aaron Katch

Tech Report #55 March 2008 (PDF)
Posterior Computation for Hierarchical Dirichlet Process Mixture Models: Application to Genetic Association Studies of Quantitative Traits in the Presence of Population Stratification
By: Nicholas M. Pajewski and Prakash Laud, PhD

 

  • STRUCTDPM.c (TXT): C code for implementing Bayesian Semiparametric modeling of Genetic Association Studies of Quantitative Traits in the Presence of Population Stratification.
  • STRUCTDPM_readme (PDF): is an instruction file for the program.

 

Tech Report #54 September 2005 (PDF)
Can we compute fMRI brain activation directly from k-space?
By: Daniel B. Rowe

Tech Report #53 December 2005 (PDF)
Multiple Treatments and Propensity Scores
By: Rodney Sparapani and Prakash Laud

Tech Report #52 July 2005 (PDF)
Models and Applications of Multivariate Statistical Analysis in fMRI
By: Daniel B. Rowe and Raymond G. Hofmann

Tech Report #51 July 2005 (PDF)
Correlated Noise of Fourier Reconstructed fMRI Data
By: Daniel B. Rowe and Raymond G. Hofmann

Tech Report #50 December 2004
A Non-Linear Model for Phase-Only fMRI data
By: Christopher P. Meller and Daniel B. Rowe

Tech Report #49 June 2004 (updated July 2010)
Some SAS macros for BUGS/WinBUGS data
By: Rodney Sparapani---More on SAS Macros

Tech Report #48 November 2004 (PDF)
A complex-valued fMRI data model for both magnitude and phase
By: Daniel B. Rowe

Tech Report #47 July 2004 (PDF)
A Complex fMRI Activation Model with a Temporally Varying Phase
By: Daniel B. Rowe and Brent R. Logan

Tech Report #46 May 2004 (PDF)
On Estimating the Parameters of the Complex fMRI Time Course Model
By Daniel B. Rowe

Tech Report #45 February 2004 (PDF)
An fMRI Activation Method Using Complex Data
By: Daniel B. Rowe and Brent R. Logan

Tech Report #44 2003 (PDF)
Additive hazards Markov regression models illustrated with bone marrow transplant data
By: Youyi Shu and John Klein

Tech Report #43 September 2003 (PDF)
Multiple Endpoints: An Overview and New Developments
By: Ajit C. Tamhane and Brent R. Logan

Tech Report #42 May 2003 (PDF)
The Effect of Correlation and Error Rate Specification On Thresholding Methods in fMRI Analysis
By: Brent R. Logan and Daniel B. Rowe

Tech Report #41 January 2003 (PDF)
fMRI Neurologic Synchrony Measures For Alzheimer's Patients With Monte Carlo Critical Values
By: Daniel B. Rowe

Tech Report #40 November 2002 (PDF)
Multivariate Regression Generalized Likelihood Ratio Tests for FMRI Activation
By: Daniel B. Rowe

Tech Report #39 June 2002 (PDF)
Computing FMRI Activations: Coefficients and t-Statistics by Detrending and Multiple Regression
By: Daniel B. Rowe and Steven W. Morgan

Tech Report #33 August 1999 (PDF)
A SAS Macro for the Positive Stable Frailty Model
By: Y. Shu and J. P. Klein

Tech Report #30 June 1998 (PDF)
Modeling Covariate Adjusted Mortality Relative to a Standard Population : Does Bone Marrow Transplantation Provide a Cure?
By: P. K. Andersen, M. M. Horowitz, J. P. Klein, G. Socie, J. Stone and M. J. Zhang

Tech Report #29 February 1998 (PDF)
Confidence Bands for the Difference of Two Survival Curves Under Proportional Hazards Model
By: M. J. Zhang and J. P. Klein

Tech Report #25 March 1997 (PDF)
Comparing Reference Charts for Cross-section and Longitudinal Data
By: T. H. Scheike, M. J. Zhang and A. Juul

Tech Report #24 February 1997 (PDF)
Grouped Failure Times, Tied Failure Times : Two Contributions to the Encyclopedia of Biostatistics
By: M. J. Zhang

Tech Report #23 January 1997 (PDF)
Testing for Center Effects in Multicenter Survival Studies: A Monte Carlo Comparison of Fixed and Random Effects Tests
By: P. K. Andersen, J. P. Klein and M. Zhang

Tech Report #22 December 1996 (PDF)
Survival Distributions and their Characteristics, a Contribution to the Encyclopedia of Biostatistics
By: J. P. Klein

Tech Report #21 December 1996 (PDF)
Determining when the Survival Rates of Two Treatments are the Same Based on a Censored Data Regression Model
By: J. P. Klein and M. Zhang

Tech Report #20 December 1996 (PDF)
Analysis of Variance with Structural Zeroes
By: T. H. Chelius and R. G. Hoffmann

Tech Report #19 September 1996 (PDF)
Modeling Multiple Nonlinear Time Series: A Graphical Approach to the Transfer Function
By: R. G. Hoffmann

Tech Report #18 September 1996 (PDF)
A SAS Macro for the Additive Hazards Regression Model
By: A. Howell and J. P. Klein

Tech Report #17 September 1996 (PDF)
Analysis of Survival Data : A Comparison of Three Major Statistical Packages (SAS, SPSS, BMDP)
By: C. J. Pelz and J. P. Klein

Tech Report #15 August 1996 (PDF)
Modeling Multistate Survival Illustrated in Bone Marrow Transplantation
By: J. P. Klein and C. Qian

Tech Report #13 July 1996 (PDF)
Confidence Regions for the Equality of Two Survival Curves
By: J. P. Klein and M. Zhang

Tech Report #12 January 1996 (PDF)
The Role of Frailty Models and Accelerated Failure Time Models in Describing Heterogeneity due to Omitted Covariates
By: N. Keiding, P. K. Andersen and J. P. Klein

Tech Report #10 September 1995 (PDF)
Estimation of Variance in Cox's Regression Model with Gamma Frailties
By: P. K. Andersen, J. P. Klein, K. M. Knudsen and R. T. y Palacios

Tech Report #9 July 1995 (PDF)
Predictive Specification of Prior Model Probabilities in Variable Selection
By: P. W. Laud and J. G. Ibrahim

Tech Report #6 November 1994 (PDF)
Predictive Model Selection
By: P. W. Laud and J. G. Ibrahim

Tech Report #5 August 1994 (PDF)
Sheppard's Correction for Grouping in Cox's Proportional Hazards Model
By: I. McKeague and M. Zhang

Tech Report #4 August 1994 (PDF)
Fitting Cox's Proportional Hazards Model Using Grouped Survival Data
By: I. McKeague and M. Zhang

Tech Report #3 August 1994 (PDF)
Statistical Challenges in Comparing Chemotherapy and Bone Marrow Transplantation as a Treatment for Leukemia
By: J. P. Klein and M. Zhang

Tech Report #2 December 1993 (PDF) -- (Table 5.1 (PDF)
Effects of Model Misspecification in Estimating Covariate Effects in Survival Analysis for Small Sample Sizes
By: Y. Li, J. P. Klein and M. L. Moeschberger

Tech Report #1 December 1993 (PDF)
Graphical Models for Panel Studies, Illustrated on Data from the Framingham Heart Study
By: J. P. Klein, N. Keiding and S. Kreiner

Alumni Information

PhD Alumni:

Yushu Shi, PhD 2017
Advisor: Prakash Laud, PhD
Thesis: Weibull Mixture Models for Regression in the Context of Time-to-Event Data
Employment: MD Anderson Cancer Center

Michael Martens, PhD 2017
Advisor: Brent Logan, PhD
Thesis: Group Sequential Design and Sample Size Calculations for Covariate Adjusted Competing Risks and Survival Analysis
Employment: EMMES Corporation

Ying Zhang, PhD 2016
Advisor: Mei-Jie Zhang, PhD
Thesis: Inference of Transition Probabilities in Multi-state Models Adaptive Inverse Probability Censoring Weighting Technique
Employment after graduation: Merck

Jianing Li, PhD 2015
Advisor: Mei-Jie Zhang, PhD
Thesis: Treatment Effect Adjustment and Model Diagnosis for Competing Risks Data
Employment after graduation: Merck

Yanzhi Wang, PhD 2014
Advisor: Brent Logan, PhD
Thesis: Generalized Linear Mixed Models for Correlated Time to Event Data Using Pseudo-Values

Peng He, PhD, 2014
Advisor: Mei-Jie Zhang, PhD
Thesis: Bias reduction by using covariate-adjusted censoring weights for survival and competing risks data
Employment after graduation: Amgen (Thousand Oaks, CA)

Kristin Ellis, PhD, 2013
Advisor: Aniko Szabo, PhD
Thesis: Developing Methods to Categorize Survival Data
Employment after graduation: Procter & Gamble

Franco Mendolia, PhD, 2013
Advisor: Tao Wang, PhD
Thesis: Pseudo-Observation Regression in the Presence of Left Truncation
Employment after graduation: German Aerospace Center (DLR)

Shuyuan Mo, PhD 2011
Advisor: Brent Logan, PhD
Thesis: Inference in the Presence of Crossing Survival Curves
Employment after graduation: Novartis

Changbin Guo, PhD 2011
Advisor: John Klein, PhD
Thesis: Regression Models for Association in Clustered Survival Data Based on Pseudo-Observations
Employment after graduation: SAS

Rodney Sparapani, PhD 2011
Advisor: Prakash Laud, PhD
Thesis: Generalized Linear Mixed Models in health Services Research with Large Data Banks: A Bayesian Implementation
Employment after graduation: The Medical College of Wisconsin

Xiaolin Fan, PhD 2008
Advisor: Prakash Laud, PhD
Thesis: Bayesian Nonparametric Inference for Competing Risks Data
Employment after graduation: Novartis

Nicholas Pajewski, PhD 2008
Advisor: Prakash Laud, PhD
Thesis: Bayesian Semiparametric Hierarchical Models for Genetic Association Studies in the Presence of Population Structure and Multiplicity

Yinghua Zhang, PhD 2007
Advisor: John P. Klein, PhD
Thesis: Selecting Between the Cox and Aalen Model for Censored Survival Data

Jingxia Liu, PhD 2007
Advisor: Mei-Jie Zhang, PhD
Thesis: Utilizing Propensity Scores to Test Treatment Effects in Survival Data

Xu Zhang, PhD 2005
Advisor: Mei-Jie Zhang, PhD
Thesis: Inference for Cumulative Incidence Function with Right Censored and/or Left Truncated Competing Risks Data

Leiyan Lu, PhD 2005
Advisor: John P. Klein, PhD
Thesis: Explained Variation in Survival Analysis and Hypothesis Testing for Current Leukemia Free Survival

Hong Wang, PhD 2004
Advisor: John P. Klein, PhD
Thesis: Inference for the Shared Power Variance Function Frailty Model and the Correlated Inverse Gaussion Frailty Model

Ruta Bajorunaite, PhD 2003
Advisor: John P. Klein, PhD
Thesis: Comparison of Failure Probabilities in the Presence of Competing Risks

Matthew Hayat, PhD 2002
Advisor: Prakash Laud, PhD
Thesis: Bayesian Methods for Longitudinal Data

Youyi Shu, PhD 2001
Advisor: John P. Klein, PhD
Thesis: Multistate Survival Models Theory And Applications

Jingtao Wu, PhD 2001,
Advisor: John P. Klein, PhD
Thesis: Statistical Methods For Discretizing A Continuous Covariate In A Censored Data Regression Model

MS Alumni:

Junmin Shi, MS 2012

Aaron Katch, MS 2012

Mikesh Shivakoti, MS 2012

Leann Watts, MS 2011

Victoria Rajamanickam, MS 2007

Manoj Thakur, MS 2006

Alain DeClaux Tallasouop, MS 2006

Lauren Cerull, MS 2005
Thesis: Assessing Discharge Location for Geriatric Fall Patients

Christopher Meller, MS 2004
Thesis: Modeling fMRI Series Using a Nonlinear Method

Youyi Shu, MS 2001
Thesis: A SAS Macro for the Positive Frailty Model

Huajian Tang, MS 2000
Thesis: A Regression-Based Transmission / Disequilibrium Test For Binary Traits Using A Logit Link Function

Zhiyuan Xu, MS 1998
Thesis: A SAS Macro for the Score Test of Homogeneity for Survival Data

Philip Rowlings, MS 1997
Thesis: A Revised Severity Index For Acute Graft-Versus-Host Disease Following HLA-Identical Sibling Bone Marrow Transplants For Leukemia

Thomas Chelius, MS 1997
Thesis: Analysis Of Variance With Structural Zeroes

Jian Chen, MS 1997
Thesis: A SAS Module For The Inverse Gaussian Frailty Model

Jeff Gudmonson, MS 1997
Thesis: A SAS Macro For The GAMMA Frailty Model

James Gapinski, MS 1996
Thesis: The Evaluation And Application Of Methods For Detecting Unnecessary Hospital Stay In Patients With Congestive Heart Failure

Alicia Howell, MS 1996
Thesis: A SAS Macro For The Additive Hazards Regression Model

Geraldine Brown, MS, 1996
Thesis: Effects of Prognostic Factors on Cataract in Family Data From The Beaver Dam Eye Study

Astrid Müller, MS 1995
Thesis: Evaluation Of Efficacy Of Endoscopy In Reducing Mortality And Morbidity Of Colorectal Cancer Using

Corey Pelz, MS 1995
Thesis: Analysis Of Survival Data: A Comparison Of Three Major Statistical Packages (SAS, SPSS, BMDP)

Graduate School of Biomedical Sciences General CAMPUS CONTACT INFORMATION

Mailing Address:
MCW Graduate School
8701 Watertown Plank Road
Milwaukee, WI 53226


(414) 955-8218
(414) 955-6555 (fax)
gradschool@mcw.edu

Department of Biostatistics

(414) 955-8280
phdbiostatistics@mcw.edu
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