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
Wang, Xiaofei; Wang, Xiaoyi; Hodgson, Lydia et al. (2017) Validation of Progression-Free Survival as a Surrogate Endpoint for Overall Survival in Malignant Mesothelioma: Analysis of Cancer and Leukemia Group B and North Central Cancer Treatment Group (Alliance) Trials. Oncologist 22:189-198
Zhang, Danjie; Chen, Ming-Hui; Ibrahim, Joseph G et al. (2017) Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data with Applications to Cancer Clinical Trials. J Comput Graph Stat 26:121-133
Kang, Suhyun; Lu, Wenbin; Song, Rui (2017) Subgroup detection and sample size calculation with proportional hazards regression for survival data. Stat Med 36:4646-4659
Zhao, Jingkang; Li, Dongshunyi; Seo, Jungkyun et al. (2017) Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding. Res Comput Mol Biol 10229:336-352
Silva, Grace O; Siegel, Marni B; Mose, Lisle E et al. (2017) SynthEx: a synthetic-normal-based DNA sequencing tool for copy number alteration detection and tumor heterogeneity profiling. Genome Biol 18:66
Stinchcombe, Thomas E; Zhang, Ying; Vokes, Everett E et al. (2017) Pooled Analysis of Individual Patient Data on Concurrent Chemoradiotherapy for Stage III Non-Small-Cell Lung Cancer in Elderly Patients Compared With Younger Patients Who Participated in US National Cancer Institute Cooperative Group Studies. J Clin Oncol 35:2885-2892
Li, Zhiguo (2017) Comparison of adaptive treatment strategies based on longitudinal outcomes in sequential multiple assignment randomized trials. Stat Med 36:403-415
Ding, Jieli; Lu, Tsui-Shan; Cai, Jianwen et al. (2017) Recent progresses in outcome-dependent sampling with failure time data. Lifetime Data Anal 23:57-82
Liang, Baosheng; Tong, Xingwei; Zeng, Donglin et al. (2017) SEMIPARAMETRIC REGRESSION ANALYSIS OF REPEATED CURRENT STATUS DATA. Stat Sin 27:1079-1100
Kong, Dehan; Maity, Arnab; Hsu, Fang-Chi et al. (2017) Rejoinder to ""A note on testing and estimation in marker-set association study using semiparametric quantile regression kernel machine"". Biometrics :

Showing the most recent 10 out of 462 publications