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.
|Acharya, Chaitanya R; McCarthy, Janice M; Owzar, Kouros et al. (2016) Exploiting expression patterns across multiple tissues to map expression quantitative trait loci. BMC Bioinformatics 17:257|
|Laber, Eric B; Zhao, Ying-Qi; Regh, Todd et al. (2016) Using pilot data to size a two-arm randomized trial to find a nearly optimal personalized treatment strategy. Stat Med 35:1245-56|
|Li, Zhiguo; Owzar, Kouros (2016) Fitting Cox Models with Doubly Censored Data Using Spline-Based Sieve Marginal Likelihood. Scand Stat Theory Appl 43:476-486|
|Wang, Xiaofei; Berry, Mark F (2016) Risk calculators are useful but.... J Thorac Cardiovasc Surg 151:706-7|
|Wang, Xuefeng; Chen, Mengjie; Yu, Xiaoqing et al. (2016) Global copy number profiling of cancer genomes. Bioinformatics 32:926-8|
|Ivanova, Anastasia; Wang, Yunfei; Foster, Matthew C (2016) The rapid enrollment design for Phase I clinical trials. Stat Med 35:2516-24|
|Zhang, Daowen; Sun, Jie Lena; Pieper, Karen (2016) Bivariate Mixed Effects Analysis of Clustered Data with Large Cluster Sizes. Stat Biosci 8:220-233|
|Schifano, Elizabeth D; Wu, Jing; Wang, Chun et al. (2016) Online Updating of Statistical Inference in the Big Data Setting. Technometrics 58:393-403|
|Lizotte, Daniel J; Laber, Eric B (2016) Multi-Objective Markov Decision Processes for Data-Driven Decision Support. J Mach Learn Res 17:|
|Minsker, Stanislav; Zhao, Ying-Qi; Cheng, Guang (2016) Active Clinical Trials for Personalized Medicine. J Am Stat Assoc 111:875-887|
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