News
RECENT FACULTY AWARDED GRANTS
Professor Peter Thall and Assistant Professor Ruitao Lin (MPI) were awarded an R01 grant (1R01CA261978-01) titled Bayesian Methods for Complex Precision Biotherapy Trials in Oncology.
Professor Liang Li received a CPRIT grant for Data Management and Analysis Core for Comparative Effectiveness Research on Cancer in Texas, for which he is Co-PI.
Assistant Professor Christine Peterson received the National Science Foundation Division of Mathematical Sciences DMS- 2113602 and DMS- 2113557 grants for Collaborative Research: Covariate-driven Approaches to Network Estimation, for which she is Co-PI.
Professor J. Jack Lee will serve as the Core Director for The University of Texas MD Anderson Cancer Center SPORE in Melanoma, Core 3 Biostatistics and Bioinformatics. The goal is to provide comprehensive service to guide experiment design to optimize quantitative data analysis, while maintaining statistical justification and results interpretation through sound experimental design principles tailored to each project. This will include data analyses and contributions to the interpretation of results via written reports and interactions with investigators.
RECENT NEWS
MD ANDERSON BIOSTATISTICS AT JSM 2021
Faculty, analysts, postdoctoral fellows and graduate researchers from the Biostatistics department organized sessions, gave presentations, and/or had coauthored work presented at the JSM 2020 virtual conference, August 8-12, 2021.
JSM Invited Sessions
Christine Peterson, Scalable Bayesian Inference of Networks and Covariate Effects; Time-varying Graphical Models for Microbiome Data; Bayesian Interaction Selection for Meta-Analysis with Rare and Censored Events.
JSM Contributed Sessions
Jujie Zhao, Ying Yuan, A Bayesian Platform Trial Design to Simultaneously Evaluate Multiple Drugs in Multiple Indications with Mixed Endpoints. Ying Yuan, Elastic Meta-Analytic-Predictive Prior for Dynamically Borrowing Information from Historical Data with Application to Biosimilar Clinical Trials. Ruitao Lin, Ying Yuan, Optimal Dose Finding Using Bayesian Utility with Toxicity and Efficacy Endpoints. Peng Wei, A Survival Mediation Model with Bayesian Model Averaging; Predicting Outcomes of Phase III Oncology Trials with Bayesian Mediation Modeling. Liangliang Zhang, Kim-Anh Do, Christine Peterson, Bayesian Zero-Constrained Regression Modeling with Structured Prior for Microbiome Feature Selection. Ruitao Lin, BOIN12: Bayesian Optimal Interval Phase I/II Trial Design for Utility-Based Dose-Finding. Jing Ning, Assessing the Predictive Performance of Biomarkers in the Presence of Competing Risks Due to Transplantation; Landmark Approach to Improve Prediction Accuracy of Long-Term Survival by Incorporating Time Information from Multiple Short-Term Events. Jing Ning, Yu Shen, Liang Li, Statistical Modeling of Longitudinal Medical Cost Trajectory. Liang Li, Predicting the Risk of Clinical Events Using Longitudinal Data: The Generalized Landmark Analysis. Min Jin Ha, Bayesian Inference on Multilayered Non-Normal Graphical Models.
JSM Posters / Speed Sessions
Tanbin Rahman, A Sparse Negative Binomial Mixture Model for Clustering RNA-Seq Count Data. Jian Wang, Inverse Probability Weighting-Based Mediation Analysis for Microbiome Data. Liang Li, Wenhao Li, A Comparison of Two Approaches for Dynamic Prediction: Joint Modeling and Landmark Modeling. Ryan Sun, Inference for Set-Based Effects in Genetic Association Studies with Interval-Censored Outcomes.
MD ANDERSON BIOSTATISTICS AT ENAR 2020 VIRTUAL SPRING MEETING
ENAR Invited Session: Ziyi Li, Recent Advances in Statistical Methods for Complex High-throughput Data.
MD ANDERSON BIOSTATISTICS AT ENAR 2019 SPRING MEETING
Faculty, analysts, postdoctoral fellows and graduate researchers from the Biostatistics department organized sessions, gave presentations, and/or had coauthored work presented at ENAR 2019 in Philadelphia, Pennsylvania, USA, March 24-27, 2019.
ENAR Invited Sessions
Liang Li (speaker): Dynamic Prediction of Competing Risk Events using Landmark Sub-Distribution Hazard Model with Multivariate Longitudinal Biomarkers. Jeffrey S. Morris (speaker), Hojin Yang, Michelle Miranda, etc.: Bayesian Regression Models for Big Spatially or Longitudinally Correlated Functional Data. Peter F. Thall, etc.: Bayesian Variable Selection for a Semi-Competing Risks Model with Three Hazard Functions. Ying Yuan (speaker): A Bayesian Adaptive Design for Biosimilar Clinical Trials Using Calibrated Power Prior.
ENAR Contributed Sessions
Jing Ning, Yu Shen, etc.: Regression Analysis of Combined Incident and Prevalent Cohort. Hai Shu (presenter), Peng Wei, etc.: High-Dimensional Decomposition-Based Canonical Correlation Analysis. Jack Lee, etc.: BayesESS: An R package and a Web-based Application for Quantifying the Impact of Parametric Priors in Bayesian Analysis. Jeffrey S. Morris, etc.: Function-on-Scalar Quantile Regression with Application to Mass Spectrometry Proteomics Data. Ying Yuan, etc.: A Continuous Reassessment Method for Pediatric Phase I Clinical Trials. Jing Ning, etc.: Quantifying the Evidence of Selective Publishing in Network Meta-Analysis: An EM Algorithm-Based Approach. Suyu Liu, Peter Thall and Ying Yuan, etc.: Group Sequential Enrichment Designs Based on Adaptive Regression of Response and Survival Time on High Dimensional Covariates. Hojin Yang (speaker), Jeffrey S. Morris, etc.: Regression Analyses of Distributions using Quantile Functional Regression. Liangliang Zhang (speaker): Bayesian Variable Selection in Regression with Compositional Covariates
ENAR Posters
Yushu Shi (presenter), Liangliang Zhang, Kim-Anh Do, Robert Jenq and Christine Peterson: A Bayesian Approach for Flexible Clustering of Microbiome Data. Research Statistical Analyst Dawen Sui (presenter) and Mary Elizabeth Edgerton: Body Mass Index and Breast Cancer Survival: A Censored Quantile Regression Analysis. Fang Xia (presenter), Jing Ning, Liang Li, Xuelin Huang, etc.: A Signature Enrichment Design with Bayesian Adaptive Randomization for Cancer Clinical Trials. Yanhong Zhou (presenter), J. Jack Lee and Ying Yuan: Utility-based Seamless Phase I/II Trial Design to Identify the Optimal Biological Dose for Targeted and Immune Therapies.
RECENT HONORS
Li elected AAAS Fellow
Liang Li, Ph.D., was elected as a fellow in the American Association for the Advancement of Science (AAAS). This honor recognizes invaluable contributions to science and technology via a process of peer selection since 1874.
Lee elected AAAS Fellow
J. Jack Lee, Ph.D., was elected as a fellow in the American Association for the Advancement of Science (AAAS). A process of selection by peers in the organization since 1874, this honor recognizes invaluable contributions to science and technology.
MD Anderson Biostatistics at the ENAR 2019 Spring Meeting
Faculty, analysts, postdoctoral fellows and graduate researchers from the Biostatistics department organized sessions, gave presentations, and/or had coauthored work presented at ENAR 2019 in Philadelphia, Pennsylvania, USA, March 24-27, 2019.
ENAR Invited Sessions
Liang Li (speaker): Dynamic Prediction of Competing Risk Events using Landmark Sub-Distribution Hazard Model with Multivariate Longitudinal Biomarkers. Jeffrey S. Morris (speaker), Hojin Yang, Michelle Miranda, etc.: Bayesian Regression Models for Big Spatially or Longitudinally Correlated Functional Data. Peter F. Thall, etc.: Bayesian Variable Selection for a Semi-Competing Risks Model with Three Hazard Functions. Ying Yuan (speaker): A Bayesian Adaptive Design for Biosimilar Clinical Trials Using Calibrated Power Prior.
ENAR Contributed Sessions
Jing Ning, Yu Shen, etc.: Regression Analysis of Combined Incident and Prevalent Cohort. Hai Shu (presenter), Peng Wei, etc.: High-Dimensional Decomposition-Based Canonical Correlation Analysis. Jack Lee, etc.: BayesESS: An R package and a Web-based Application for Quantifying the Impact of Parametric Priors in Bayesian Analysis. Jeffrey S. Morris, etc.: Function-on-Scalar Quantile Regression with Application to Mass Spectrometry Proteomics Data. Ying Yuan, etc.: A Continuous Reassessment Method for Pediatric Phase I Clinical Trials. Jing Ning, etc.: Quantifying the Evidence of Selective Publishing in Network Meta-Analysis: An EM Algorithm-Based Approach. Suyu Liu, Peter Thall and Ying Yuan, etc.: Group Sequential Enrichment Designs Based on Adaptive Regression of Response and Survival Time on High Dimensional Covariates. Hojin Yang (speaker), Jeffrey S. Morris, etc.: Regression Analyses of Distributions using Quantile Functional Regression. Liangliang Zhang (speaker): Bayesian Variable Selection in Regression with Compositional Covariates
ENAR Posters
Yushu Shi (presenter), Liangliang Zhang, Kim-Anh Do, Robert Jenq and Christine Peterson: A Bayesian Approach for Flexible Clustering of Microbiome Data. Research Statistical Analyst Dawen Sui (presenter) and Mary Elizabeth Edgerton: Body Mass Index and Breast Cancer Survival: A Censored Quantile Regression Analysis. Fang Xia (presenter), Jing Ning, Liang Li, Xuelin Huang, etc.: A Signature Enrichment Design with Bayesian Adaptive Randomization for Cancer Clinical Trials. Yanhong Zhou (presenter), J. Jack Lee and Ying Yuan: Utility-based Seamless Phase I/II Trial Design to Identify the Optimal Biological Dose for Targeted and Immune Therapies.
Professor Jeffrey Morris is the Core Director for MD Anderson Cancer Center SPORE in Gastrointestinal Cancer, Core 2 Biostatistics and Bioinformatics. This effort will provide modeling, simulations and data analysis to enable the specific aims of related Cores by developing and adapting innovative statistical and bioinformatics methods pertinent to translational cancer studies. This will include generating related reports and facilitating project investigators in publishing results. [ST1]
[ST1]New…edit and then upload and add to newsletter. Morris likely to change.
Two biostatistics faculty are Fellows of the Society for Clinical Trials
Professors Peter F. Thall (2014) and J. Jack Lee (2017) are Fellows of the Society for Clinical Trials. This fellowship honors society members who have made significant contributions to the advancement of clinical trials and to the society.
Eight biostatistics faculty are ASA fellows
Eight of the department's faculty members have attained the prestigious honor of being named a Fellow in the American Statistical Association: Donald A. Berry (1986), Kim-Anh Do (2006), Yu Shen (2007), J. Jack Lee (2008), Sanjay Shete (2012), Peter F. Thall (2015), Xuelin Huang (2017), and Ying Yuan (2017).
Open Positions
Faculty Position
Assistant, Associate, or Full Professor
Exceptional candidates are sought for multiple tenured/tenure-track faculty positions at the Assistant, Associate, or full Professor level with The University of Texas MD Anderson Cancer Center, Department of Biostatistics. Applicants should demonstrate prowess in interdisciplinary, collaborative scientific research. The department will consider those developing methodologies with application to biomedical research in various areas, with particular interest in early detection and cancer screening, clinical trial design, imaging, and computer-intensive methodology—including machine learning, and integrative analyses of multiplatform high-dimensional data, such as genomic, proteomic, and microbiome analysis. A Ph.D. in statistics, biostatistics, or a related field is required. To apply, visit Research Faculty Recruitment. September 15, 2021.
Join the team that is once again number one in cancer care at The University of Texas MD Anderson Cancer Center, Department of Biostatistics. Ranked by U.S. News and World Report’s annual “Best Hospitals” survey, the institution has held the top two positions since the start of the list. From Nobel-level collaborations to programs that dramatically accelerate the conversion of scientific discovery into clinical realities, your work will help make cancer history.
Exceptional candidates are sought for multiple tenured/tenure-track faculty positions at the Assistant, Associate, or full Professor level. Applicants should demonstrate prowess in interdisciplinary, collaborative scientific research. The department will consider those developing methodologies with application to biomedical research in various areas, with particular interest in early detection and cancer screening, clinical trial design, imaging, and computer-intensive methodology—including machine learning, and integrative analyses of multiplatform high-dimensional data, such as genomic, proteomic, and microbiome analysis. A Ph.D. in statistics, biostatistics, or a related field is required.
The department has 19 faculty members, 37 masters and doctoral-level research analysts, and 10 postdoctoral fellows. Faculty members are actively involved in collaborative and methodological research in diverse areas such as clinical trial design, cancer screening and early detection, bioinformatics, genomic pathway analysis, network analysis, and integrative modeling of multiple types of complex data. This includes high-dimensional omic data, functional data analysis, Bayesian methodology, longitudinal and survival analysis, statistical genetics, population health research, and behavioral/social statistics.
Our faculty collaborate with world-class cancer scientists, such as Dr. James Allison—the 2018 Nobel Prize winner for medicine—in all cancer areas and research levels. Opportunities to support large-scale studies and programs include the Moon Shots Program, which brings together the top aspects of academia and industry to swiftly translate data into patient benefits. Their large, important cancer data sets require quantitative input with impact potential. Faculty also partner with our affiliated biostatistics doctoral programs at the University of Texas, Texas A&M University, and Rice University. Our department offers strong resources, including an in-house quantitative research computing team specializing in database design, web-based clinical trial support, scientific programming, and software engineering. For specifics, visit: https://biostatistics.mdanderson.org. Direct further questions to the selection committee chair.
The institution offers competitive salaries and an outstanding personal and professional benefits package. Houston is one of the world’s most innovative and diverse cities, with great neighborhoods; competitive private and public schools; a dynamic music, theater and sports scene; highly acclaimed museums; international cuisine; and year-round outdoor recreational activities.
MD Anderson is an equal opportunity employer and does not discriminate on the basis of race, color, national origin, gender, sexual orientation, age, religion, disability or veteran status, except where such distinction is required by law. All positions are security sensitive and subject to examination of criminal history record information. MD Anderson is a smoke-free and drug-free environment.
Consideration of applications will continue until the positions are filled. Applicants should send a cover letter outlining the relevance of their research experience and interests to the position, a curriculum vitae, a brief statement of current and proposed research, and three letters of recommendation to:
Faculty Search Committee
Department of Biostatistics
The University of Texas MD Anderson Cancer Center
P.O. Box 301402
Houston, TX 77230-1402
Email: biostat-search@mdanderson.orgBiostatistician Position
Successful candidates will join a team of biostatisticians working on a wide variety of collaborative projects in cancer research. The University of Texas MD Anderson Cancer Center supports and promotes professional growth and mentoring, as well as continuing education, creativity, and discovery.
Responsibilities
Collaborate with clinicians and scientists to plan meaningful studies, statistically analyze and communicate/document the results:
- Assess relevant literature and existing data
- Perform simulations and complex statistical analyses using advanced statistical methods and programming
- Prepare statistical considerations for clinical trial designs or grant applications
- Perform biostatistical reviews of research protocols
- Collaborate on development of new statistical methodology
- Prepare written reports, including reports of data for committee and scientific meetings
- Collaborate with investigators on manuscripts
- Make original contributions to research projects
- Take initiative in professional activities
- If hired as or promoted to a senior position, participate in training and supervising junior statistical analysts
To be considered, send a letter of interest, curriculum vitae and three (3) letters of reference (include contact information for references) to dqs-stat-job@mdanderson.org
Minimum Qualifications:
- Master's degree in biostatistics, bioinformatics, statistics, computing, or related field
- Working knowledge of Windows and/or UNIX operating systems
- Experience in statistical programming and analysis
- Excellent oral and written communication skills in English
- Experience with mainframe and/or PC databases, document processing and statistical software such as SAS and S-Plus
Desired Qualifications:
- Experience writing reports and analytical sections of grant applications
- Extensive experience in statistical consulting, data analysis, design and management of clinical trials in the biomedical or cancer research setting
Salary: Competitive; commensurate with experience
EEO & Employment Eligibility
It is the policy of The University of Texas MD Anderson Cancer Center to provide equal employment opportunity without regard to race, color, religion, age, national origin, sex, sexual orientation, gender identity/expression, disability, veteran status, genetic information or any other basis protected by MD Anderson policy or by federal, state or local laws, unless such distinction is required by law. All positions at MD Anderson Cancer Center are security sensitive and subject to examination of criminal history record information. MD Anderson Cancer Center provides a smoke-free and drug-free environment.
Postdoctoral Fellow Position
None at this time.