The principal objectives of the training grant are to educate promising pre- and postdoctoral students to be biostatistical scientists in cancer. Ten predoctoral and two postdoctoral trainees are requested. The term biostatistical science refers to the use of the methods of statistics, probability, computer science and mathematics to increase our knowledge and understanding of biomedical phenomena. The course of study for predoctoral students includes probability, statistics, and computing. All students are required to take a concentration in courses related to cancer. There is a close relationship with Dana-Farber Cancer Institute where many of the students are in residence. During the first and second summer periods in the program, predoctoral students are involved in a special program which allows them to participate in the research activities of the Dana-Farber Cancer Institute. After the first summer, many of the students take up residence at the Dana-Farber Cancer Institute and continue their projects. One of the goals for the program is to enable the predoctoral students to carry out dissertation research on new statistical methodology. Nearly all of the dissertation research is on methods related to cancer research. The postdoctoral students may be those who have completed a doctorate in statistics or biostatistics who are carrying out postdoctoral research on cancer related activities or doctoral recipients from another field who generally attend courses the first year and start their postdoctoral research the second year. The postdoctoral students are closely involved with the practice of biostatistics in cancer and are in residence at the Dana-Farber Cancer Institute. An important part of the training is a working seminar on quantitative issues in cancer research. The seminar serves as a forum to discuss current issues in cancer research. ? ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Institutional National Research Service Award (T32)
Project #
2T32CA009337-26A2
Application #
7123566
Study Section
Special Emphasis Panel (ZCA1-RTRB-A (M1))
Program Officer
Damico, Mark W
Project Start
1979-06-01
Project End
2011-07-31
Budget Start
2006-08-24
Budget End
2007-07-31
Support Year
26
Fiscal Year
2006
Total Cost
$440,776
Indirect Cost
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
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
Braun, Danielle; Gorfine, Malka; Parmigiani, Giovanni et al. (2017) Propensity scores with misclassified treatment assignment: a likelihood-based adjustment. Biostatistics 18:695-710
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

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