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.
|Wang, Xiaofei; Wang, Xiaoyi; Hodgson, Lydia et al. (2017) Validation of Progression-Free Survival as a Surrogate Endpoint for Overall Survival in Malignant Mesothelioma: Analysis of Cancer and Leukemia Group B and North Central Cancer Treatment Group (Alliance) Trials. Oncologist 22:189-198|
|Zhang, Danjie; Chen, Ming-Hui; Ibrahim, Joseph G et al. (2017) Bayesian Model Assessment in Joint Modeling of Longitudinal and Survival Data with Applications to Cancer Clinical Trials. J Comput Graph Stat 26:121-133|
|Kang, Suhyun; Lu, Wenbin; Song, Rui (2017) Subgroup detection and sample size calculation with proportional hazards regression for survival data. Stat Med 36:4646-4659|
|Zhao, Jingkang; Li, Dongshunyi; Seo, Jungkyun et al. (2017) Quantifying the Impact of Non-coding Variants on Transcription Factor-DNA Binding. Res Comput Mol Biol 10229:336-352|
|Silva, Grace O; Siegel, Marni B; Mose, Lisle E et al. (2017) SynthEx: a synthetic-normal-based DNA sequencing tool for copy number alteration detection and tumor heterogeneity profiling. Genome Biol 18:66|
|Stinchcombe, Thomas E; Zhang, Ying; Vokes, Everett E et al. (2017) Pooled Analysis of Individual Patient Data on Concurrent Chemoradiotherapy for Stage III Non-Small-Cell Lung Cancer in Elderly Patients Compared With Younger Patients Who Participated in US National Cancer Institute Cooperative Group Studies. J Clin Oncol 35:2885-2892|
|Li, Zhiguo (2017) Comparison of adaptive treatment strategies based on longitudinal outcomes in sequential multiple assignment randomized trials. Stat Med 36:403-415|
|Ding, Jieli; Lu, Tsui-Shan; Cai, Jianwen et al. (2017) Recent progresses in outcome-dependent sampling with failure time data. Lifetime Data Anal 23:57-82|
|Liang, Baosheng; Tong, Xingwei; Zeng, Donglin et al. (2017) SEMIPARAMETRIC REGRESSION ANALYSIS OF REPEATED CURRENT STATUS DATA. Stat Sin 27:1079-1100|
|Kong, Dehan; Maity, Arnab; Hsu, Fang-Chi et al. (2017) Rejoinder to ""A note on testing and estimation in marker-set association study using semiparametric quantile regression kernel machine"". Biometrics :|
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