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.

Public Health Relevance

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.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
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Special Emphasis Panel (ZCA1-RPRB-7)
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University of North Carolina Chapel Hill
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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 :
Jung, Sin-Ho; Lee, Ho Yun; Chow, Shein-Chung (2018) Statistical Methods for Conditional Survival Analysis. J Biopharm Stat 28:927-938
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
Chung, Yunro; Ivanova, Anastasia; Hudgens, Michael G et al. (2018) Partial likelihood estimation of isotonic proportional hazards models. Biometrika 105:133-148
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 :
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 :
Gao, Fei; Zeng, Donglin; Wei, Helen et al. (2018) Estimating Treatment Effects for Recurrent Events in the Presence of Rescue Medications: An Application to the Immune Thrombocytopenia Study. Stat Biosci 10:473-489

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