This is a competing renewal application that requests continued support for providing pre- and postdoctoral trainees with strong methodological and practical training in quantitative cancer research at the Harvard Public School of Public Health and Dana-Farber Cancer Institute (DFCI). This training program, now in its 30th year, draws upon a distinguished faculty, consisting of biostatisticians and computational biologists, as well as world renowned experts in cancer treatment and research. Its overarching goal is to provide the trainees with all essential elements of training needed to successfully undertake modern cancer research. The specific goals of this training program are to train students and postdoctoral fellows to be (1) quantitative scientists in cancer research, who are capable of using probability, statistics, computer science and mathematics to increase our knowledge and understanding of cancer;(2) strong team leaders/players as well as excellent communicators in a cancer research environment, who can effectively disseminate their research results and assume active roles in the design, analysis and interpretation of cancer clinical trials, cancer prevention trials and cancer genomic studies. All predoctoral students supported by this training grant are required to take a concentration in cancer-related courses. During their first and second summer periods in the program, students are required to participate in research activities of the Dana-Farber Cancer Institute (DFCI), performed under the supervision of faculty mentor/trainers affiliated in the program. Afterwards, many of these students will take up residence at the DFCI and continue their research in cancer, which eventually evolves into their dissertation projects. All the postdoctoral fellows are closely involved with the practice of quantitative sciences in cancer and are in residence at the DFCI. All trainees are required to actively participate in the a working group seminar series on quantitative issues in cancer research, which serves as a primary forum at Harvard to discuss current issues and challenges on this topic. This proposal requests funding to support 10 pre-doctoral students and 1 post-doctoral fellow for 5 years.

Public Health Relevance

The training program is to train students and postdoctoral fellows to be high quality quantitative researchers who are capable of conducting cutting edge methodological and collaborative research in cancer clinical trials, computational biology, cancer genomics, cancer epidemiology and population science. Also, the program trains quantitative researchers to become strong team leaders/players as well as excellent communicators in a cancer research environment, and to enable them to effectively disseminate their research results and to assume active roles in the design, analysis and interpretation of, for example, cancer genomic studies, cancer clinical trials and cancer prevention trials.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Institutional National Research Service Award (T32)
Project #
5T32CA009337-32
Application #
8294526
Study Section
Subcommittee G - Education (NCI)
Program Officer
Damico, Mark W
Project Start
1979-06-01
Project End
2016-07-31
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
32
Fiscal Year
2012
Total Cost
$503,909
Indirect Cost
$25,475
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Braun, Danielle; Gorfine, Malka; Katki, Hormuzd A et al. (2018) Nonparametric Adjustment for Measurement Error in Time-to-Event Data: Application to Risk Prediction Models. J Am Stat Assoc 113:14-25
Patil, Prasad; Parmigiani, Giovanni (2018) Training replicable predictors in multiple studies. Proc Natl Acad Sci U S A 115:2578-2583
Sun, Ryan; Horiguchi, Miki; Wei, Lee-Jen (2018) Interpreting the Benefit of Trifluridine/Tipiracil in Metastatic Colorectal Cancer With Respect to Progression-Free Survival and Overall Survival. J Clin Oncol 36:1378-1379
Braun, Danielle; Gorfine, Malka; Parmigiani, Giovanni et al. (2017) Propensity scores with misclassified treatment assignment: a likelihood-based adjustment. Biostatistics 18:695-710
Patro, Rob; Duggal, Geet; Love, Michael I et al. (2017) Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14:417-419
Collado-Torres, Leonardo; Nellore, Abhinav; Frazee, Alyssa C et al. (2017) Flexible expressed region analysis for RNA-seq with derfinder. Nucleic Acids Res 45:e9
Chipman, J; Braun, D (2017) Simpson's paradox in the integrated discrimination improvement. Stat Med 36:4468-4481
Love, Michael I; Hogenesch, John B; Irizarry, Rafael A (2016) Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation. Nat Biotechnol 34:1287-1291
Li, Shuli; Gray, Robert J (2016) Estimating treatment effect in a proportional hazards model in randomized clinical trials with all-or-nothing compliance. Biometrics 72:742-50
Haneuse, Sebastien; Bogart, Andy; Jazic, Ina et al. (2016) Learning About Missing Data Mechanisms in Electronic Health Records-based Research: A Survey-based Approach. Epidemiology 27:82-90

Showing the most recent 10 out of 90 publications