The overall scientific goal of this ambitious program project is to develop highly innovative methods for cancer clinical trials that can hasten successful introduction of effective new therapies into practice. The method of approach is to leverage recent advances in statistical and computational science to create new clinical trial designs and data analysis tools that resolve many of the key scientific limitations of current clinical trial methodology. The projects focus on practical design and analysis problems in Phase II and Phase 111 clinical trials, the problem of missing data and efficient use of prognostic information, postmarketing surveillance and comparative effectiveness research using clinical trial data, pharmacogenetics and individualized therapies, and the potential of dynamic treatment regimens to improve cancer treatment. The proposed clinical trial design and analysis innovations have the potential to change the prevailing clinical trial paradigm and greatly increase the rate of discovery and translation of new treatments into clinical practice. Our multi-institutional approach includes an effective and energetic process for intense, coordinated implementation, communication and dissemination of results, including developing new software for practical implementation of the newly developed methods. Our comprehensive and novel approach will lead to significant improvements in cancer clinical trial practice that will result in improved health of cancer patients.

Public Health Relevance

The proposed program project aims to dramatically improve the efficiency of the cancer clinical trial process in order to improve the health and longevity of cancer patients. This is extremely important to public health since almost all biomedical advances in cancer treatment must pass through the clinical trial process before becoming accepted clinical practice.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
5P01CA142538-05
Application #
8643480
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Wu, Roy S
Project Start
2010-04-01
Project End
2015-03-31
Budget Start
2014-04-01
Budget End
2015-03-31
Support Year
5
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Zhou, Jie; Zhang, Jiajia; Lu, Wenbin (2018) Computationally Efficient Estimation for the Generalized Odds Rate Mixture Cure Model with Interval-Censored Data. J Comput Graph Stat 27:48-58
Butler, Emily L; Laber, Eric B; Davis, Sonia M et al. (2018) Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules. Biometrics 74:18-26
Wong, Kin Yau; Zeng, Donglin; Lin, D Y (2018) Efficient Estimation for Semiparametric Structural Equation Models With Censored Data. J Am Stat Assoc 113:893-905
Wang, Charlotte; Tzeng, Jung-Ying; Wu, Pei-Zhen et al. (2018) Reexamining Dis/Similarity-Based Tests for Rare-Variant Association with Case-Control Samples. Genetics 209:105-113
Wu, Jing; Ibrahim, Joseph G; Chen, Ming-Hui et al. (2018) Bayesian Modeling and Inference for Nonignorably Missing Longitudinal Binary Response Data with Applications to HIV Prevention Trials. Stat Sin 28:1929-1963
Li, Zhiguo; Wang, Xiaofei; Wu, Yuan et al. (2018) Sample size calculation for studies with grouped survival data. Stat Med 37:3904-3917
Wang, Chun; Chen, Ming-Hui; Wu, Jing et al. (2018) Online updating method with new variables for big data streams. Can J Stat 46:123-146
Ni, Ai; Cai, Jianwen (2018) Tuning Parameter Selection in Cox Proportional Hazards Model with a Diverging Number of Parameters. Scand Stat Theory Appl 45:557-570
Teran Hidalgo, Sebastian J; Wu, Michael C; Engel, Stephanie M et al. (2018) Goodness-Of-Fit Test for Nonparametric Regression Models: Smoothing Spline ANOVA Models as Example. Comput Stat Data Anal 122:135-155
Jung, Sin-Ho (2018) Phase II cancer clinical trials for biomarker-guided treatments. J Biopharm Stat 28:256-263

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