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.
|Jung, Sin-Ho; Lee, Ho Yun; Chow, Shein-Chung (2018) Statistical Methods for Conditional Survival Analysis. J Biopharm Stat 28:927-938|
|Jiang, Yuchao; Wang, Rujin; Urrutia, Eugene et al. (2018) CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing. Genome Biol 19:202|
|Kim, Soyoung; Zeng, Donglin; Cai, Jianwen (2018) Analysis of multiple survival events in generalized case-cohort designs. Biometrics :|
|Chung, Yunro; Ivanova, Anastasia; Hudgens, Michael G et al. (2018) Partial likelihood estimation of isotonic proportional hazards models. Biometrika 105:133-148|
|Liang, Shuhan; Lu, Wenbin; Song, Rui (2018) Deep advantage learning for optimal dynamic treatment regime. Stat Theory Relat Fields 2:80-88|
|Chen, Xiaolin; Cai, Jianwen (2018) Reweighted estimators for additive hazard model with censoring indicators missing at random. Lifetime Data Anal 24:224-249|
|Yang, Yuchen; Huh, Ruth; Culpepper, Houston W et al. (2018) SAFE-clustering: Single-cell Aggregated (From Ensemble) Clustering for Single-cell RNA-seq Data. Bioinformatics :|
|Nasution, Marlina D; Wang, Xiaofei (2018) Statistical issues and advances in cancer precision medicine research. J Biopharm Stat 28:215-216|
|Wu, Yuan; Chambers, Christina D; Xu, Ronghui (2018) Semiparametric sieve maximum likelihood estimation under cure model with partly interval censored and left truncated data for application to spontaneous abortion. Lifetime Data Anal :|
|Ibrahim, Joseph G; Kim, Sungduk; Chen, Ming-Hui et al. (2018) Bayesian multivariate skew meta-regression models for individual patient data. Stat Methods Med Res :962280218801147|
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