Core B: Computational Resources and Dissemination Core PROJECT SUMMARY The overall goal of this program project is to develop innovative statistical methods for cancer clinical trials. To facilitate the acceptance and usage of new methods by the cancer research community, it is essential that the methods be evaluated rigorously and implemented in professional, robust, user-friendly software. Documenta- tion and demonstrations of new software must be readily available to researchers and accessible to users with different backgrounds and experience levels. The Computational Resources and Dissemination Core (Core B) will be responsible for the critical functions required to achieve these objectives. The Core will work closely with project investigators to develop efficient, robust code that implements newly developed methods for the purpose of evaluation in extensive simulation experiments and for their application to representative data. The Core will develop robust, reliable, well-documented, and user-friendly software packages that implement new, fully-evaluated methods for public dissemination to the cancer research community on the program project web- site. This effort will include the development of web-based interactive tutorials for all previously developed and future project software, which will enable users of all skill levels to test drive the software's use. The Core will be responsible for upgrading the program project website toward enhancing outreach and accessibility of all resources, including software, tutorials, publications, and other project products, in collaboration with Core A. Finally, the Core will facilitate a comprehensive effort to develop a program project reproducibility resource, in which all project publications will be linked to the codes used for all published simulation studies and application examples. Core B will carry out all of these functions according to best practices.

Public Health Relevance

Core B: Computational Resource and Dissemination Core PROJECT NARRATIVE New statistical methods, such as those to be developed in this program project, must be evaluated rigorously before they can be adopted by the cancer research community. Once tested, methods must be implemented in user-friendly software that is available to researchers on a public website, and the methods and software must be made accessible to users with different levels of experience and backgrounds. In collaboration with project investigators, Core B will facilitate these efforts.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
2P01CA142538-06
Application #
8794725
Study Section
Special Emphasis Panel (ZCA1-RPRB-B (O1))
Project Start
2010-04-01
Project End
2020-03-31
Budget Start
2015-05-15
Budget End
2016-03-31
Support Year
6
Fiscal Year
2015
Total Cost
$361,863
Indirect Cost
$21,464
Name
University of North Carolina Chapel Hill
Department
Type
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Yang, Shu; Tsiatis, Anastasios A; Blazing, Michael (2018) Modeling survival distribution as a function of time to treatment discontinuation: A dynamic treatment regime approach. Biometrics 74:900-909
Chen, Jingxiang; Zhang, Chong; Kosorok, Michael R et al. (2018) Double Sparsity Kernel Learning with Automatic Variable Selection and Data Extraction. Stat Interface 11:401-420
Kang, Suhyun; Lu, Wenbin; Zhang, Jiajia (2018) ON ESTIMATION OF THE OPTIMAL TREATMENT REGIME WITH THE ADDITIVE HAZARDS MODEL. Stat Sin 28:1539-1560
Maity, Arnab; Zhao, Jing; Sullivan, Patrick F et al. (2018) Inference on phenotype-specific effects of genes using multivariate kernel machine regression. Genet Epidemiol 42:64-79
Sibley, Alexander; Li, Zhiguo; Jiang, Yu et al. (2018) Facilitating the Calculation of the Efficient Score Using Symbolic Computing. Am Stat 72:199-205
Liu, Ying; Wang, Yuanjia; Kosorok, Michael R et al. (2018) Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens. Stat Med 37:3776-3788
Wong, Kin Yau; Zeng, Donglin; Lin, D Y (2018) Efficient Estimation for Semiparametric Structural Equation Models With Censored Data. J Am Stat Assoc 113:893-905
Zhou, Jie; Zhang, Jiajia; Lu, Wenbin (2018) Computationally Efficient Estimation for the Generalized Odds Rate Mixture Cure Model with Interval-Censored Data. J Comput Graph Stat 27:48-58
Butler, Emily L; Laber, Eric B; Davis, Sonia M et al. (2018) Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules. Biometrics 74:18-26
Li, Zhiguo; Wang, Xiaofei; Wu, Yuan et al. (2018) Sample size calculation for studies with grouped survival data. Stat Med 37:3904-3917

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