The overarching goal of this ambitious program project continues to be to develop innovative, transformative statistical methods for cancer clinical trials that have the potential to hasten successful introduction of new therapies and treatment strategies into practice. Advances in the biologic, genomic, statistical, and computational sciences hold great promise for the development of personalized cancer treatments. Our multi-institutional, interdisciplinary team of investigators will leverage these advances to create new clinical trial designs and data analysis approaches that resolve many of the key limitations of current statistical methods and that maximize the effectiveness of clinical trials for personalized cancer medicine. In addition, we will foster translation of these methods into practice, including carrying out pilot animal and human studies based on the new methodology. The program will achieve these objectives through five, interrelated research projects carried out by investigators with complementary expertise in the statistical, computational, and clinical sciences from three institutions. The first four projects focus on developing new trial designs and analysis methods that integrate biomarkers for efficient discovery of new, personalized treatments; on creating methods for analysis of existing data on biomarkers and patient reported outcomes to inform and improve the design of future studies; developing methods for maximizing the power of pharmacogenomics for identifying biomarkers and candidate individualized therapies; and creating new methods for discovering and validating sequential, personalized decision-making strategies for cancer treatment. The fifth project will integrate the methods into novel preclinical and clinical studies of pancreatic cancer. Our comprehensive approach involves an energetic and coordinated process for implementation, communication, and dissemination of results, including development of professional, public-use software and associated tutorials; workshops and other outreach mechanisms; and program-sponsored symposia and events, to accelerate the adoption of the methods in practice. The proposed clinical trial design and analysis innovations have the potential to effect a paradigm shift in the way cancer clinical trials are conducted for discovery and validation of personalized medicine. This comprehensive, multi-institutional effort will lead to significant innovations in cancer clinical trial practice that will result in improved health of cancer patients.

Public Health Relevance

This program project is focused on the development of new methods for cancer clinical trials that have the potential to accelerate the discovery of effective new cancer treatments and strategies for personalizing cancer therapy to the unique features of individual patients. The methods will be of great importance to public health, as almost all advances in cancer treatment must pass through the clinical trial process before being adopted in clinical practice.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
2P01CA142538-06
Application #
8794722
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Song, Min-Kyung H
Project Start
2010-04-01
Project End
2020-03-31
Budget Start
2015-05-15
Budget End
2016-03-31
Support Year
6
Fiscal Year
2015
Total Cost
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Liang, Shuhan; Lu, Wenbin; Song, Rui et al. (2018) Sparse concordance-assisted learning for optimal treatment decision. J Mach Learn Res 18:
Wang, Yu-Bo; Chen, Ming-Hui; Kuo, Lynn et al. (2018) A New Monte Carlo Method for Estimating Marginal Likelihoods. Bayesian Anal 13:311-333
Laber, Eric B; Wu, Fan; Munera, Catherine et al. (2018) Identifying optimal dosage regimes under safety constraints: An application to long term opioid treatment of chronic pain. Stat Med 37:1407-1418
Diao, Guoqing; Dong, Jun; Zeng, Donglin et al. (2018) Biomarker threshold adaptive designs for survival endpoints. J Biopharm Stat 28:1038-1054
Davenport, Clemontina A; Maity, Arnab; Sullivan, Patrick F et al. (2018) A Powerful Test for SNP Effects on Multivariate Binary Outcomes using Kernel Machine Regression. Stat Biosci 10:117-138
Chen, Stephanie T; Xiao, Luo; Staicu, Ana-Maria (2018) A Smoothing-based Goodness-of-Fit Test of Covariance for Functional Data. Biometrics :
Chen, Kun; Mishra, Neha; Smyth, Joan et al. (2018) A Tailored Multivariate Mixture Model for Detecting Proteins of Concordant Change Among Virulent Strains of Clostridium Perfringens. J Am Stat Assoc 113:546-559
Wang, Lan; Zhou, Yu; Song, Rui et al. (2018) Quantile-Optimal Treatment Regimes. J Am Stat Assoc 113:1243-1254
Hager, Rebecca; Tsiatis, Anastasios A; Davidian, Marie (2018) Optimal two-stage dynamic treatment regimes from a classification perspective with censored survival data. Biometrics :
Lachos, Victor H; A Matos, Larissa; Castro, Luis M et al. (2018) Flexible longitudinal linear mixed models for multiple censored responses data. Stat Med :

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