Current Fellows (2012-2013)
Kevin Galinsky, Daniel Schlauch, Sixing Chen, David Valken-LeDuc, and Ryan Sun are first year students in the Biostatistics doctoral program. They are currently focused on coursework, and are currently taking core biostatistics and epidemiology courses. They are currently working on summer projects:
Adam Sullivan is a third year student (dissertation advisor: Tyler Vanderweele) in the Biostatistics doctoral program. His research this past year has focused on analyzing data for three different papers dealing with multiple aspects of Religious and Spiritual Care given to end of life cancer patients. This research examined the perceptions of religious and spiritual care from the vantage of patients, nurses and physicians. One specifically centered on the impact of a dedicated service on palliative care. Another focused on how radiation oncologists evaluate and incorporate life expectancy estimates into the treatment of palliative cancer patients. Lastly, Adam is analyzing data concerning subjects who have had a metastatic cancer tumor removed. This project then links how much chemotherapy was given to make sure the tumor did not grow back and the time until the tumor returns. The project will also incorporate the amount of chemotherapy given as well as the extent of the area around the tumor that was treated.
Ian Barnett is a third year student (dissertation advisor: Xihong Lin) in the Biostatistics doctoral program. He is currently working on bringing the higher criticism test, original designed for high dimensional data to detect sparse signals, to the SNP-set genetic association testing realm. This is useful when detecting when there are only a few mutations related to disease within a gene. He is applying this new method to lung cancer GWAS and breast cancer GWAS data sets in order to identity the top associated genes.
Christina McIntosh is a fourth year student (dissertation advisor: Giovanni Parmigiani) in the Biostatistics doctoral program. She is currently working on a project that compares two methods of analyzing pedigrees for an association between a disease and gene. This project assesses the empirical power of the Family Based Association Test (FBAT) and the Pedigree Based Association Test (PBAT). Also, the empirical power of the ascertainment conditions of the pedigrees are compared. Additionally, she is working on expanding a survival model that involves twins to a Bayesian non-parametric framework. A new project that involves quantifying cancer "immunity" from a statistical framework.
Danielle Braun is a fifth year student (dissertation advisor: Giovanni Parmigiani) in the Biostatistics doctoral program. Currently, she is working on a project to develop methods for handling misreported family history in Mendelian risk prediction models for cancer. This project introduces an approach that allows adjustment for misreporting of family history by modeling the measurement error process in the survival context, and using this to weigh the risk prediction estimates. The team proposes different models for the measurement error process using data from UCI. The goal is to extend BRCAPRO, a Mendelian risk prediction model for breast and ovarian cancer, to handle misreported family history. They will extend BRCAPRO using the methods they develop. They also will illustrate the results using specific cases, as well as validate using CFR data.
Norman Huang is a fifth year student (dissertation advisor: Cheng Li) in the Biostatistics doctoral program. He has completed all his courses and has just defended his dissertation. Norman's dissertation primarily concerns how gene expression and pathway information can be used to predict patient response in MM patients. Predicting patient response has a significant amount of clinical value since better response rates naturally correlates with better survival. However, while the utility to predict response is clear, the main problem is that it is difficult to do so. Much of the problem arises from the fact that many well-established clinical characteristics that are reliable predictors in prognosis and survival (i.e. tumor stage, size, patient & age) end up being unreliable in a response prediction setting. Thus, the propose is to use a multitude of genomic factors to tackle this problem in hope that this alternative source of information can lead to better prediction results.
Cristian Tomasetti (mentor Professor Giovanni Parmigiani, HSPH/DFCI) is a Postdoctoral Fellow in the Department of Biostatistics at the Harvard School of Public Health and the Dana-Farber Cancer Institute. Dr. Tomasetti received his PhD in Applied Mathematics and a MA in Mathematics, from the University of Maryland, College Park. He will be moving to a faculty position in the department of Oncology and Biostatistics at Johns Hopkins University this fall. The focus of Dr. Tomasetti's work is on the development of mathematical models of cancer evolution, development of drug resistance and cell stem dynamics. During the past two years he has been provided with the opportunity to work on cancer genetics, a fascinating new direction, and to statistically analyze various sequencing datasets.