This application is for continuation of a training program in biostatistics with a specific focus on cancer research. This training program supports six students each year. The practice of biostatistics is changing, especially in the area of cancer research. Discoveries over the past two decades into the molecular, biochemical, and genetic components of cell growth and development have changed our understanding of what makes a normal cell become cancerous, and then later metastasize. Given numerous available treatments for many cancers, each with efficacy for a subset of patients, this information has the potential to be leveraged to personalize therapy so each patient can be given the treatment that is most likely to benefit them given their cancer's molecular signature. Early detection and prevention efforts have also made inroads in numerous cancers, and demonstrate continuing promise to reduce mortality and morbidity due to cancer. The development of new methods for surveying a tumor molecularly and genetically have led to enormous, complex data sets whose relevant biological information is difficult to extract, and the intricacy and exceeding sensi- tivty of many of these methods raise considerable challenges in terms of designing studies that obtain reproducible measurements and replicable results. Designing clinical trials that can incorporate such information and take it into account in patient treatment is challenging, and requires many quantitative issues be carefully handled. Multidisciplinary research teams are at the heart of modern approaches to fighting cancer, and biostatisticians and other quantitative scientists find themselves as an increasingly crucial part of such teams, given the fundamental quantitative nature of many of the modern biomedical research challenges. Such teamwork requires that the statistician be conversant with molecular biologists, pathologists, and, of course, clinical oncologists if the group of co-investigators is to succeed. Effective biostatisticl collaboration thus requires broad training in statistics, probability, computational methods, as well as cancer biology and medical ethics. The Department of Statistics, Rice University, and the departments in the Division of Quantitative Sciences at the University of Texas M. D. Anderson Cancer Center have joined forces with their collaborators in the clinical and basic sciences to developed a unique training program that combines their respective strengths to train biostatisticians in cancer research. The goal of this Training Program in Biostatistics 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: - Rigorous training in statistics and probability; - Practical experience in basic and clinical cancer research; - Training in biological aspects of cancer and medical research ethics. Students in this program follow a standard course of study expected of Ph.D. students in the Department of Statistics at Rice, with additional coursework in biostatistics, biology, and ethics, as well as special seminars and workshops at both institutions. 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 necessary for modem cancer research. Summer internships and laboratory rotations provide practical experience in the student's training.

Public Health Relevance

Combining the educational 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, allowing translation of therapeutic theories to clinical application more quickly.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Institutional National Research Service Award (T32)
Project #
5T32CA096520-10
Application #
8681375
Study Section
Subcommittee B - Comprehensiveness (NCI)
Program Officer
Lim, Susan E
Project Start
2002-07-01
Project End
2018-08-31
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
10
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Rice University
Department
Biostatistics & Other Math Sci
Type
Biomed Engr/Col Engr/Engr Sta
DUNS #
City
Houston
State
TX
Country
United States
Zip Code
77005
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 125:601-15
Allen, Genevera I; Amoroso, Nicola; Anghel, Catalina et al. (2016) Crowdsourced estimation of cognitive decline and resilience in Alzheimer's disease. Alzheimers Dement 12:645-53
Galloway-Peña, Jessica R; Smith, Daniel P; Sahasrabhojane, Pranoti et al. (2016) The role of the gastrointestinal microbiome in infectious complications during induction chemotherapy for acute myeloid leukemia. Cancer 122:2186-96
Peterson, Christine B; Stingo, Francesco C; Vannucci, Marina (2016) Joint Bayesian variable and graph selection for regression models with network-structured predictors. Stat Med 35:1017-31
Snowdon, Kyle A; Smeak, Daniel D; Chiang, Sharon (2016) Risk Factors for Dehiscence of Stapled Functional End-to-End Intestinal Anastomoses in Dogs: 53 Cases (2001-2012). Vet Surg 45:91-9
Peterson, Christine B; Stingo, Francesco C; Vannucci, Marina (2015) Bayesian Inference of Multiple Gaussian Graphical Models. J Am Stat Assoc 110:159-174
Youmans, Bonnie P; Ajami, Nadim J; Jiang, Zhi-Dong et al. (2015) Characterization of the human gut microbiome during travelers' diarrhea. Gut Microbes 6:110-9
Chiang, Sharon; Levin, Harvey S; Haneef, Zulfi (2015) Computer-automated focus lateralization of temporal lobe epilepsy using fMRI. J Magn Reson Imaging 41:1689-94
Chiang, Sharon; Stern, John M; Engel Jr, Jerome et al. (2015) Structural-functional coupling changes in temporal lobe epilepsy. Brain Res 1616:45-57

Showing the most recent 10 out of 45 publications