PhD, Biostatistics, University of Michigan

Research interests include: statistical and bioinformatic methodology, next-generation sequencing data, adaptive clinical trials, longitudinal binary data; cancer biology, epidemiology, genetics, and bioinformatics; proteomics; survival analysis; teaching biostatistics

Honors and service include: Fellow, American Statistical Association (elected 2010); fellow, American Association for the Advancement of Science (elected 2016); associate editor for statistics, JAMA Oncology (since 2015)

More information: biostat.app.vumc.org/YuShyr 

PhD, Statistics, University of Auckland, New Zealand

Research interests include: developing novel statistical methods for problems arising in public health studies; developing semiparametric models that are flexible and robust to model misspecification; two-phase designs; measurement-error problems; ordinal data analysis

PhD, Computer Science, University of Minnesota

Research interests include: bioinformatics, causal inference, cancer genomics, computational biology, data science, high-dimensional statistics, machine learning

More information: amirasiaee.com

MS, Applied Statistics, Louisiana State University

Research interests include: statistical evidence, effective statistical collaboration, clinical trial methodology

More information: biostat.app.vumc.org/DanAyers

PhD, Cell Biology, Vanderbilt University

Responsibilities include: working with the software development team at the Center for Quantitative Sciences, overseeing and directing individual projects as well as the entire group.

MS, Biostatistics, New York Medical College

Research interests include: clinical epidemiology, worksite wellness/productivity, outcomes, health services, clinical pharmacology, efficiency in clinical and translational research, predicting and preventing complications

More information: biostat.app.vumc.org/DanielByrne

PhD, Biomathematics, North Carolina State University

Research interests include: survival analysis, mixed model and longitudinal data analysis, two-stage adaptive design for phase II clinical trials, model/process validation for high-dimensional data

More information: biostat.app.vumc.org/HeidiChen

PhD, Biostatistics, University of North Carolina at Chapel Hill

Research interests include: missing data, survival analysis (especially time-varying effect and treatment switching), Bayesian methods in big data, penalized approach for 1<n/p<10, developing statistical methods for using electronic health records (EHRs) and genetics information in precision medicine, cancer, ophthalmology, infection disease, cardiovascular diseases, suicide

More information: http://biostat.app.vumc.org/QingxiaChen

PhD, Biostatistics, Johns Hopkins University

Research interests include: constructing a system for pharmacokinetics, pharmacodynamics, and pharmacogenomics studies using electronic health records (EHRs), which would provide the foundation for dose-optimization support system for precision medicine; developing medication dosing extraction algorithms from EHRs, which would also benefit medication-related studies in pharmacoepidemiology

More information: biostat.app.vumc.org/LeenaChoi

PhD, statistics/mathematics education, The Ohio State University

Research interests include: medical education assessment; research and statistical collaboration; questionnaire design; regression modeling; statistical consulting; statistics and mathematics education; mixed methodology; the statistics and mathematics achievement gap

More information: biostat.app.vumc.org/MarioDavidson

PhD, chemical engineering, Vanderbilt University

Responsibilities include: collaborating with basic science and clinical research scientists in different fields, focusing on writing statistical analysis plan for grant applications and helping them with experimental designs, analyzing statistical data, interpreting results, and preparing manuscripts. She also works on clinical trial reporting at ClinicalTrials.gov for Vanderbilt-Ingram Cancer Center (VICC), performs data analysis for epidemiology studies and high-dimensional proteomic/genomic/metabolomics research, and collaborates with Peabody at Vanderbilt on education-related statistical data analysis.

PhD, biostatistics, Johns Hopkins Universit

Research interests include: epidemiology, breast disease, cancer, genetics, hormonal replacement therapy, screening, power and sample calculations, data management, data quality assurance, clinical trials, stopping rules, foundations of statistical inference, likelihood methods

Selected honors: Fellow, American Statistical Association (elected 2009); Ernest W. Goodpasture Award for Groundbreaking Research, 2003

More information: biostat.app.vumc.org/WilliamDupont

PhD, biostatistics, Vanderbilt University

Research interests include: developing semi- and nonparametric methods of estimating correlation in survival data; causal inference methods (specifically Mendelian Randomization analysis); risk factors in veterans; survival analysis; rank association measures; Bayesian modeling 

More information: https://svetlanaeden.github.io/

PhD, biostatistics, University of Washington

Research interests include: statistical analysis methods for longitudinal and survival data, with scientific focus on estimation of exposure-outcome associations or evaluation of prediction accuracy; cardiovascular disease; cancer epidemiology; health economics

MS, statistics, Pennsylvania State University

Research interests include: applying computational and numerical skills to modeling complex data, rational integration of genomic healthcare testing (RIGHT), reproducible manuscripts

More information: http://biostat.app.vumc.org/wiki/Main/ShawnGarbett

PhD, cell biology, University of Massachusetts Medical School

MPH, University of California, Berkeley

Research interests include: clinical asthma research (adult and pediatric), infectious disease/global health, rheumatology/immunology, statistical methods in epidemiology, predictive modeling, analysis of electronic health records, multidimensional data, observational studies, causal inference studies, maternal and environmental influences on pediatric respiratory health, teaching biostatistics, clinical pharmacology, HIV and AIDS, cancer, clinical trials, diabetes

PhD, Statistics, University of Pennsylvania

Research interests include: restricted randomization methods and sample size adaptive randomization for clinical trials, inverse probability weighted and doubly robust estimators, sensitivity analyses for unmeasured confounding, foundational tools for better statistical inference, comparative effectiveness of anti-hyperglycemic medications in an EHR derived cohort of 400 thousand veterans with type 2 diabetes, effects of acute kidney injury in an EHR derived cohort of 17 million veterans, randomized controlled trials for smoking cessation and for diabetes management

More information: biostat.app.vumc.org/RobertGreevy

PhD, Statistics, Colorado State University

Research interests include: health service research, public health research, diabetes, environmental biostatistics, causal inference, Bayesian inference, regression splines, semi parametric estimation

Honors and service include: Data safety and monitoring board member, Preliminary Investigation of optimaL Oxygen Targets (PILOT) trial; graduate program and staff promotions committees

More information: biostat.app.vumc.org/AmberHackstadt

Dr. Harrell received his PhD in Biostatistics from UNC in 1979. Since 2003 he has been Professor of Biostatistics, Vanderbilt University School of Medicine, and was the department chairman from 2003-2017. He is Expert Statistial Advisor for the Office of Biostatistics for FDA CDER. He is Associate Editor of Statistics in Medicine, a member of the Scientific Advisory Board for Science Translational Medicine, a member of the Faculty of 1000 Medicine, and a member of the policy advisory board for the Journal of Clinical Epidemiology. He is a Fellow of the American Statistical Association and winner of the Association's WJ Dixon Award for Excellence in Statistical Consulting for 2014. He was the 2017 Visionary Speaker, Clinical Studies Coordinating Center, University of North Carolina Department of Biostatistics. His specialties are development of accurate prognostic and diagnostic models, model validation, clinical trials, observational clinical research, cardiovascular research, technology evaluation, pharmaceutical safety, Bayesian methods, quantifying predictive accuracy, missing data imputation, and statistical graphics and reporting.

Dr. Huang specializes in latent variable modeling, confirmatory factor analysis (CFA), structural equation modeling (SEM) and mediation analysis. He also has expertise in longitudinal data analysis, hierarchical linear models. He is proficient with Mplus statistical software.

Dr. Huang’s interests include the application of advanced statistical methodology for prevention research on substance use and HIV risk behavior. Dr. Huang has served on a number of NIH and CDC-funded studies focused on substance abuse and HIV prevention (totally approximately $10 million).

Bob Johnson is a part-time (semi-retired) faculty member whose primary role is teaching and serving on students’ graduate committees. He previously collaborated with primary care researchers with focus on cluster randomized trials and continues in an advisory role.

Dr. Kang's research interests include spatio-temporal modeling, multiple testing problems, and their applications to imaging data, in particular, fMRI data. He is also interested in multi-modal imaging data analysis, e.g., fMRI and DTI.

PhD, Statistics, University of Pittsburgh

Research interests include: group sequential methods; two-stage adaptive procedures (phase II / phase III); noninferiority / superiority / equivalence trials; statistics in basic science; statistical graphics; p value, confidence interval, unbiased estimate from a Simon's two-stage procedure

Honors and service include: Patrick G. Arbogast Collaborative Publication Awards (2014 and 2017); treasurer, American Statistical Association, Middle Tennessee Chapter

More information: biostat.app.vumc.org/TatsukiKoyama

Association for Clinical and Translational Science (ACTS) president, 2020 - 2021

PhD, Epidemiology, University of Southampton

http://biostat.app.vumc.org/ChrisLindsell

Dr. Liu completed her doctoral degree in biostatistics at the University of Michigan and a postdoctoral fellowship at Fred Hutchinson Cancer Research Center, before joining the Department of Biostatistics as an Assistant Professor at Vanderbilt University. Dr. Liu serves as the faculty biostatistician for the Department of Emergency Medicine collaboration plan and the Vanderbilt Memory & Alzheimer’s Center. Her research interests are focused on biomarker evaluation, event history data analysis with application to cardiovascular disease, cancer and Alzheimer's disease. In addition, she is also working on developing statistical methods for risk prediction modeling and its practical implementation in electronical health records system.

Dr. Liu’s research focuses on integrating multi-omics data to address fundamental biological questions. Dr. Liu develops tools and algorithms to mine genomics, transcriptomics, epigenomics and single-cell data, finds the connection between different molecular layers, and bridges the gaps between vast amount of omics data and biology.

Dr. Nian is a collaborative biostatistician with special interests in causal inference for multilevel treatments, Bayesian adaptive design for clinical trials and provider profiling.

Dr. Raman’s research is motivated by real-world problems that need to be addressed with suitable statistical methods. She serves as a statistical collaborator for the Tennessee Department of Children’s Services, Vanderbilt Center for Critical Illness, Brain Dysfunction and Survivorship, and CDC's Emerging Infections Program, and she is a co-I on multiple NIH-funded studies. Her methodological research is mostly associated with challenges encountered in her collaborative work and includes health disparity measures, provider profiling, area under the time curve, and methods to address survivor/attrition bias. In addition, Dr. Raman co-instructs the statistics course for Doctorate in Nursing Practice students at the School of Nursing.

Lauren Samuels is a collaborative biostatistician who specializes in the design and analysis of observational studies in health services research. Her cohort selection app, Visual Pruner, is available at http://biostat.app.vumc.org/VisualPruner.

More information: biostat.app.vumc.org/JonathanSchildcrout

Dr. Sheng is focusing on next generation sequencing data analysis, especially for RNAseq, DNAseq and miRNAseq data. He is also interested in bioinformatics in proteomics, glycomics and metabolimics, including algorithm development, data analysis and software implementation.

PhD, Biostatistics, University of Washington

Research interests include: causal inference, observational data analysis, ordinal data analysis, applications in HIV and other infectious diseases, measurement error, global health

Affiliate appointments include: Vanderbilt Institute for Global Health; Vanderbilt Tuberculosis Center

Honors and service include: Fellow, American Statistical Association (elected 2018); associate editor, American Journal of Epidemiology (since 2016); associate editor, Biometrics (since 2018); director of the biostatistics and biomedical informations core of TN-CFAR (since 2015)

More information: biostat.app.vumc.org/BryanShepherd

Dr. Shotwell is a collaborative statistician and biomedical researcher. His collaborative work includes topics in anesthesiology, geriatrics and quality aging, drug repurposing, and many others. Dr. Shotwell is also interested in the optimal design and statistical treatment of biomedical experiments, especially those incorporating "challenge-response" elements that are common, for example, in the study of pharmacokinetics and electrophysiology. Dr. Shotwell is active in mentoring graduate students, and has taught several courses offered by the Department of Biostatistics, including the 'Statistical Collaborations in Health Sciences' and 'Statistical Machine Learning' course.
 

Chris Slaughter focuses on flexible Bayesian regression models for longitudinal data. He works closely with a variety of collaborators in Gastroenterology, Pediatrics, and Neonatology to advance medical and basic science research.

DDS, Indiana University; PhD, Biostatistics, Vanderbilt University; MPH, Dental Public Health, A. T. Still University of Health Sciences

Research interests include:

More information: biostat.app.vumc.org/DerekSmith

Andrew Spieker received his PhD in Biostatistics from the University of Washington, prior to completing a postdoctoral fellowship at the University of Pennsylvania. His research interests are primarily centered on the integration of causal inference methodology into fields such as pharmacoepidemiologic association studies, cost-effectiveness, and preliminary HIV vaccine trials. Other research interests include the proper design, conduct, analysis, and interpretation of clinical studies; methods to diagnose and monitor disease; and risk factor identification.

Thomas Stewart is a clinical educator and collaborative statistician. He teaches in both the MSCI and MPH programs. He has collaborated with a wide variety of researchers around the medical center, with substantial support for Nephrology and AHSQC. His area of statistical methods research is missing data, with a special focus on support vector machines.

Ran Tao is interested in developing novel statistical methods to solve problems arising in the design and analysis of modern biomedical and public health studies, including genome-wide association studies, next-generation sequencing studies, and electronic health records systems. His current research topics include two-phase designs, complex survey sampling, rare-variant analysis, and risk gene prediction.

PhD, Biostatistics, University of Pennsylvania

Research interests include: psychiatry, psychology, neuroimaging, ecology

More information: simonvandekar.bitbucket.io

PhD, Biostatistics, University of South Carolina

Research interests include: 

• Multi-omics data analysis and biomarker discovery
• Clinical trial design
• Cancer research
• Prediction model development and validation

Honors and service include: Assistant editor for statistics, JAMA Oncology

More information: http://biostat.app.vumc.org/FeiYe

Dr. Yu’s methodological research focuses on models for correlated binary data, mixture models, disease clustering, measurement error correction models, and statistical methods for genetics. Some of his works are cited in Univariate Discrete Distributions by Johnson, Kemp, and Kotz (3rd edition, 2005). Before joining Vanderbilt, Dr. Yu worked more than 5 years on clinical trials at Merck Research Laboratories in Rahway, NJ. He collaborates with investigators in clinical pharmacology, epidemiology, pulmonary, and Vanderbilt Institute for Clinical and Translational Research. He leads the Bayesian adaptive clinical trial design effort in the department.

Dr. Zhao's research interest is in developing tools with statistical methods to solve problems in next generation sequencing data analysis. He has developed tools in Rna-Seq sample size estimation, DNA-Seq and RNA-Seq data quality control. He is also interested in different kinds of high-dimensional data researches, including transcriptomics, genomics, and proteomics data analysis and integrating.

MD, Shanghai Medical University; MS, Biometry, University of Texas Health Science Center

Research interests include: influenza vaccine effectiveness, disease burden analysis, studies with EHR-based prognostic decision support

More information: http://biostat.app.vumc.org/wiki/Main/YuweiZhu