This is a revised application for support of a training program in biostatistics with a focus on cancer research. 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. Multidisciplinary research teams are at the heart of modern approaches to fighting cancer, and biostatisticians more and more are a part of such teams. Effective biostatistical collaboration requires broad training in statistics, probability, computational methods, as well as cancer biology and medical ethics. 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. The Department of Statistics at Rice University and the Department of Biostatistics at the U.T.M.D. Anderson Cancer Center have recently joined forces to develop a unique 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 be expected to work side-by-side with biomedical investigators in modern cancer research.
The aims of our program are to provide trainees with: -Rigorous training in statistics and probability -Practical experience in basic and clinical cancer research at M.D. Anderson Cancer Center -Training in biological aspects of cancer and medical research ethics With faculty expertise in Bayesian methods, decision theory, cancer clinical trials, survival analysis, statistical genetics, genomics, bioinformatics, and statistical computing, trainees will receive a broad background in biostatistics necessary for modern cancer research. Combining the educational resources available at Rice University and M.D. Anderson Cancer Center with practical experience will enable trainees upon completion to make fundamental contributions to cancer research working alongside biomedical investigators.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Institutional National Research Service Award (T32)
Project #
5T32CA096520-03
Application #
6932074
Study Section
Subcommittee G - Education (NCI)
Program Officer
Gorelic, Lester S
Project Start
2003-08-01
Project End
2007-07-31
Budget Start
2005-08-01
Budget End
2007-07-31
Support Year
3
Fiscal Year
2005
Total Cost
$229,287
Indirect Cost
Name
Rice University
Department
Biostatistics & Other Math Sci
Type
Schools of Engineering
DUNS #
050299031
City
Houston
State
TX
Country
United States
Zip Code
77005
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