The Data Compilation Core (Core B) will develop and maintain a central resource of analysis-ready, annotated and documented data sets from clinical trials and related studies to be utilized by the investigators of the program. These data sets will be used to evaluate the methods developed in this program as well as to demonstrate the software developed in the Computational Resource Core (Core C). The primary source of the data will be the clinical trials and related studies of the Cancer and Leukemia Group B (CALGB), one of the major NCI-sponsored cancer cooperative groups. In addition, data from cancer research studies conducted at two large NCI-designated Comprehensive Cancer Centers, the Lineberger Comprehensive Cancer Center at UNC and the Duke Comprehensive Cancer Center, will also be utilized. This is a major advantage for the program in that the data sets provided can be exceptionally well annotated and documented, with the direct involvement of clinical and statistical scientists who were involved in the primary design and analysis of the studies.
A major disadvantage of using public data sets is that the investigator is often unable to understand the clinical and molecular data as the data are provided without appropriate documentation. Indeed, it is not possible to carry out a thorough statistical analysis of data from clinical trials without taking into account and understanding the design of the study, the specifics of the data collection process, the history of the study and the medical issues. This core will address these issues by providing analysis-ready data sets with extensive annotation and documentation.
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 |
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 |
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 |
Mathur, Ravi; Rotroff, Daniel; Ma, Jun et al. (2018) Gene set analysis methods: a systematic comparison. BioData Min 11:8 |
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