The Administrative Core (Core A) is responsible for organizing the program investigators and staff into an effective and well-coordinated team to develop and implement the statistical methods for cancer clinical trials proposed in the research projects to improve the health of cancer patients. This program is integrated across three institutions whith a lead PD/Pl at one institution (UNC-CH) and two additional PD/PIs at the other two institutions (NCSU and Duke). These three PD/PIs form an executive Committee with overall responsibility for the management and administration of the program. Each institution has an additional co-PD/PI to assist the PD/PIs with both the overall and intra-institutional administration of the program project. The Executive Committee, three co-PD/PIs. and individual project leaders form a Steering Committee which provides overall scientific guidance for the program. An External Advisory Committee of experts provides feedback to the Steering Committee on the goals and progress of the program during an annual retreat. Communication and collaboration between project investigators is facilitated with a program project wiki. Communication and dissemination of new results and software are aided with a program project web page. The matrix leadership structure of Core A maximizes the scientific integration of this multi-disciplinary and trans-institutional collaboration.
The Administrative Core (Core A) is essential to the success of the proposed project since it coordinates all administration and provides leadership for the five projects, three cores and three institutions involved in this program project. The administrative component is necessary to facilitate the science of this program project and to achieve the overall program aims, to develop new statistical methods that will improve the health of cancer patients.
|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|
|Li, Hao; Chen, Ming-Hui; Ibrahim, Joseph G et al. (2018) Bayesian inference for network meta-regression using multivariate random effects with applications to cholesterol lowering drugs. Biostatistics :|
|Gao, Fei; Zeng, Donglin; Lin, Dan-Yu (2018) Semiparametric regression analysis of interval-censored data with informative dropout. Biometrics :|
|Potthoff, Richard F (2018) Differential losses to follow-up that are outcome-dependent can vitiate a clinical trial: Simulation results. J Biopharm Stat 28:633-644|
|Wang, Ting; Wang, Xiaofei; Zhou, Haibo et al. (2018) Auxiliary variable-enriched biomarker-stratified design. Stat Med 37:4610-4635|
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