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.
|Lin, Jiaxing; Gresham, Jeremy; Wang, Tongrong et al. (2018) bcSeq: an R package for fast sequence mapping in high-throughput shRNA and CRISPR screens. Bioinformatics 34:3581-3583|
|Wu, Jing; de Castro, Mário; Schifano, Elizabeth D et al. (2018) Assessing covariate effects using Jeffreys-type prior in the Cox model in the presence of a monotone partial likelihood. J Stat Theory Pract 12:23-41|
|Mathur, Ravi; Rotroff, Daniel; Ma, Jun et al. (2018) Gene set analysis methods: a systematic comparison. BioData Min 11:8|
|Shi, Chengchun; Fan, Alin; Song, Rui et al. (2018) HIGH-DIMENSIONAL A-LEARNING FOR OPTIMAL DYNAMIC TREATMENT REGIMES. Ann Stat 46:925-957|
|Li, Wenqing; Chen, Ming-Hui; Wangy, Xiaojing et al. (2018) Bayesian Design of Non-Inferiority Clinical Trials via the Bayes Factor. Stat Biosci 10:439-459|
|Psioda, Matthew A; Soukup, Mat; Ibrahim, Joseph G (2018) A practical Bayesian adaptive design incorporating data from historical controls. Stat Med 37:4054-4070|
|Lawson, Michael T; Cho, Hunyong; Choudhury, Arkopal et al. (2018) Discussion of Laber et al. ""Optimal treatment allocations in space and time for on-line control of an emerging infectious disease"". J R Stat Soc Ser C Appl Stat 67:779-780|
|Wang, Xiaofei; Zhou, Jingzhu; Wang, Ting et al. (2018) On Enrichment Strategies for Biomarker Stratified Clinical Trials. J Biopharm Stat 28:292-308|
|Liu, Yang; He, Qianchan; Sun, Wei (2018) Association analysis using somatic mutations. PLoS Genet 14:e1007746|
|Pan, Yinghao; Cai, Jianwen; Longnecker, Matthew P et al. (2018) Secondary outcome analysis for data from an outcome-dependent sampling design. Stat Med 37:2321-2337|
Showing the most recent 10 out of 549 publications