The overall scientific goal of this ambitious Program Project is to develop innovative statistical methods for cancer clinical trials that can help to hasten successful introduction of effective new therapies into practice The Computational Resource and Dissemination Core (Core C) will carry out several critical functions related to the implementation and dissemination of the statistical methods for the design and analysis of cancer clinical trials developed in the five research projects. The Core will be tasked with developing, in close collaboration with project investigators, efficient, robust code implementing the statistical methods that can be used for evaluation of the methods in extensive simulation studies and for application of the methods to data compiled by Core B and from other sources. The Core will also be tasked with leading and facilitating, in close collaboration with project investigators, development of robust, reliable, user-friendly, and welldocumented software applications suitable for public dissemination to practitioners involved in the design and analysis of cancer clinical trials. Core C wili adopt best practices for these tasks and provide the necessary information technology and educational infrastructure to disseminate these applications.

Public Health Relevance

Before the new statistical methods for design and analysis of cancer clinical trials to be developed in this Program Project can be adopted for use in cancer research, they must be tested and evaluated, and they must be implemented in user-friendly software accessible to practitioners. Core C will collaborate closely with project investigators to facilitate these efforts.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
1P01CA142538-01
Application #
7786687
Study Section
Special Emphasis Panel (ZCA1-RPRB-7 (O1))
Project Start
2010-04-01
Project End
2015-03-31
Budget Start
2010-04-01
Budget End
2011-03-31
Support Year
1
Fiscal Year
2010
Total Cost
$280,509
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Type
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Liang, Shuhan; Lu, Wenbin; Song, Rui et al. (2018) Sparse concordance-assisted learning for optimal treatment decision. J Mach Learn Res 18:
Wang, Yu-Bo; Chen, Ming-Hui; Kuo, Lynn et al. (2018) A New Monte Carlo Method for Estimating Marginal Likelihoods. Bayesian Anal 13:311-333
Laber, Eric B; Wu, Fan; Munera, Catherine et al. (2018) Identifying optimal dosage regimes under safety constraints: An application to long term opioid treatment of chronic pain. Stat Med 37:1407-1418
Diao, Guoqing; Dong, Jun; Zeng, Donglin et al. (2018) Biomarker threshold adaptive designs for survival endpoints. J Biopharm Stat 28:1038-1054
Davenport, Clemontina A; Maity, Arnab; Sullivan, Patrick F et al. (2018) A Powerful Test for SNP Effects on Multivariate Binary Outcomes using Kernel Machine Regression. Stat Biosci 10:117-138
Chen, Stephanie T; Xiao, Luo; Staicu, Ana-Maria (2018) A Smoothing-based Goodness-of-Fit Test of Covariance for Functional Data. Biometrics :
Chen, Kun; Mishra, Neha; Smyth, Joan et al. (2018) A Tailored Multivariate Mixture Model for Detecting Proteins of Concordant Change Among Virulent Strains of Clostridium Perfringens. J Am Stat Assoc 113:546-559
Wang, Lan; Zhou, Yu; Song, Rui et al. (2018) Quantile-Optimal Treatment Regimes. J Am Stat Assoc 113:1243-1254
Hager, Rebecca; Tsiatis, Anastasios A; Davidian, Marie (2018) Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data. Biometrics :
Lachos, Victor H; A Matos, Larissa; Castro, Luis M et al. (2018) Flexible longitudinal linear mixed models for multiple censored responses data. Stat Med :

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