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.
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.
|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|
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