This application is for continuation of a training program in biostatistics with a specific focus on cancer research that supports three graduate students and two postdoctoral researchers each year. The explosion of big data in biomedical research, especially cancer, has led to new challenges and opportunities to extract information from these data to inform novel approaches for early detection, prevention, and precision therapy strategies to treat cancer. The increasingly competitive world of drug development also necessitates more efficient clinical trial designs to gather information on new treatments? efficacy and safety profiles. These challenges bring quantitative scientists, especially biostatisticians, to the forefront of cancer research to develop efficient, robust, and reproducible methods for analyzing complex biomedical data and adaptive clinical trial designs. Multidisciplinary research teams are at the heart of modern cancer research, requiring communication and mutual understanding for success. Effective biostatistical collaboration requires broad training in statistics, probability, computing, as well as cancer biology, medical ethics, and effective communication to collaborators. The Department of Statistics, Rice University, and the Department of Biostatistics and Department of Bioinformatics and Computational Biology at the University of Texas M. D. Anderson Cancer Center (UTMDACC) have joined forces with their collaborators in the clinical, basic, and population sciences to develop a unique training program that combines their respective strengths to train biostatisticians in cancer research. The goal of this Training Program is to prepare a new generation of biostatisticians who will work side-by-side with biomedical investigators in modern cancer research. Our program aims to provide trainees with: (1) Rigorous training in statistics and probability (2) Practical experience in basic and clinical cancer research (2) Training in biological aspects of cancer, medical research ethics, and effective communication. Predoctoral trainees in the program follow standard PhD coursework for students in Statistics at Rice, with additional coursework in biostatistics, biologic, ethics, and communication as well as special seminars and workshops at both institutions, plus hands-on experience in summer internship and laboratory rotations. Postdoctoral trainees have access to the same coursework and hands-on experiences, plus will gain grant- writing experience during their training. With faculty expertise in Bayesian methods, decision theory, cancer clinical trials, cancer screening, survival analysis, statistical genetics, genomics, bioinformatics, and statistical computing, trainees will receive a broad background in biostatistics for modern cancer research, with improved rigorous admissions, evaluation and development procedures to ensure success in producing researchers.

Public Health Relevance

Combining the educational and research resources available at Rice University and the M.D. Anderson Cancer Center with practical experience will enable trainees, upon completion of the program, to make fundamental contributions to cancer research working alongside biomedical investigators. Such close collaboration will lead to more efficient study designs and data analysis methodologies, enabling rapid translation of biological knowledge provided by biomedical big data and biological theories to clinical application.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Institutional National Research Service Award (T32)
Project #
Application #
Study Section
Subcommittee I - Transistion to Independence (NCI)
Program Officer
Lim, Susan E
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Rice University
Biostatistics & Other Math Sci
Biomed Engr/Col Engr/Engr Sta
United States
Zip Code
Mo, Qianxing; Shen, Ronglai; Guo, Cui et al. (2018) A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data. Biostatistics 19:71-86
Peak, Taylor; Chapple, Andrew; Coon, Grayson et al. (2018) Semi-competing risk model to predict perioperative and oncologic outcomes after radical cystectomy. Ther Adv Urol 10:317-326
Nagorski, John; Allen, Genevera I (2018) Genomic region detection via Spatial Convex Clustering. PLoS One 13:e0203007
Chiang, Sharon; Vankov, Emilian R; Yeh, Hsiang J et al. (2018) Temporal and spectral characteristics of dynamic functional connectivity between resting-state networks reveal information beyond static connectivity. PLoS One 13:e0190220
Shoemaker, Katherine; Hobbs, Brian P; Bharath, Karthik et al. (2018) Tree-based Methods for Characterizing Tumor Density Heterogeneity. Pac Symp Biocomput 23:216-227
Chapple, Andrew G; Vannucci, Marina; Thall, Peter F et al. (2017) Bayesian variable selection for a semi-competing risks model with three hazard functions. Comput Stat Data Anal 112:170-185
Galloway-Peña, Jessica R; Smith, Daniel P; Sahasrabhojane, Pranoti et al. (2017) Characterization of oral and gut microbiome temporal variability in hospitalized cancer patients. Genome Med 9:21
Wadsworth, W Duncan; Argiento, Raffaele; Guindani, Michele et al. (2017) An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data. BMC Bioinformatics 18:94
Chiang, Sharon; Levin, Harvey S; Wilde, Elisabeth et al. (2016) White matter structural connectivity changes correlate with epilepsy duration in temporal lobe epilepsy. Epilepsy Res 120:37-46
Chiang, Sharon; Cassese, Alberto; Guindani, Michele et al. (2016) Time-dependence of graph theory metrics in functional connectivity analysis. Neuroimage 125:601-615

Showing the most recent 10 out of 68 publications