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.
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.
Psioda, Matthew A; Ibrahim, Joseph G (2018) Bayesian design of a survival trial with a cured fraction using historical data. Stat Med 37:3814-3831 |
Zhou, Qingning; Cai, Jianwen; Zhou, Haibo (2018) Outcome-dependent sampling with interval-censored failure time data. Biometrics 74:58-67 |
Psioda, Matthew A; Ibrahim, Joseph G (2018) Bayesian clinical trial design using historical data that inform the treatment effect. Biostatistics : |
Shi, Chengchun; Song, Rui; Lu, Wenbin et al. (2018) Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects. J R Stat Soc Series B Stat Methodol 80:681-702 |
Isogai, Yoh; Wu, Zheng; Love, Michael I et al. (2018) Multisensory Logic of Infant-Directed Aggression by Males. Cell 175:1827-1841.e17 |
Wei, Susan; Kosorok, Michael R (2018) The Change-Plane Cox Model. Biometrika 105:891-903 |
Pan, Yinghao; Cai, Jianwen; Kim, Sangmi et al. (2018) Regression analysis for secondary response variable in a case-cohort study. Biometrics 74:1014-1022 |
Jeng, X Jessie; Lu, Wenbin; Peng, Huimin (2018) High-Dimensional Inference for Personalized Treatment Decision. Electron J Stat 12:2074-2089 |
Zhao, Junlong; Yu, Guan; Liu, Yufeng (2018) ASSESSING ROBUSTNESS OF CLASSIFICATION USING ANGULAR BREAKDOWN POINT. Ann Stat 46:3362-3389 |
Innocenti, Federico; Jiang, Chen; Sibley, Alexander B et al. (2018) Genetic variation determines VEGF-A plasma levels in cancer patients. Sci Rep 8:16332 |
Showing the most recent 10 out of 549 publications