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.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Program Projects (P01)
Project #
Application #
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Song, Min-Kyung H
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of North Carolina Chapel Hill
Biostatistics & Other Math Sci
Schools of Public Health
Chapel Hill
United States
Zip Code
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
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
Jung, Sin-Ho (2018) Phase II cancer clinical trials for biomarker-guided treatments. J Biopharm Stat 28:256-263
Li, Tengfei; Xie, Fengchang; Feng, Xiangnan et al. (2018) Functional Linear Regression Models for Nonignorable Missing Scalar Responses. Stat Sin 28:1867-1886
Pietryk, Edward W; Clement, Kiristin; Elnagheeb, Marwa et al. (2018) Intergenerational response to the endocrine disruptor vinclozolin is influenced by maternal genotype and crossing scheme. Reprod Toxicol 78:9-19
Psioda, Matthew A; Ibrahim, Joseph G (2018) Bayesian design of a survival trial with a cured fraction using historical data. Stat Med 37:3814-3831
Zhou, Qingning; Cai, Jianwen; Zhou, Haibo (2018) Outcome-dependent sampling with interval-censored failure time data. Biometrics 74:58-67
Psioda, Matthew A; Ibrahim, Joseph G (2018) Bayesian clinical trial design using historical data that inform the treatment effect. Biostatistics :
Shi, Chengchun; Song, Rui; Lu, Wenbin et al. (2018) Maximin Projection Learning for Optimal Treatment Decision with Heterogeneous Individualized Treatment Effects. J R Stat Soc Series B Stat Methodol 80:681-702

Showing the most recent 10 out of 549 publications