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.
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.
| Wang, Xiaofei; Zhou, Jingzhu; Wang, Ting et al. (2018) On Enrichment Strategies for Biomarker Stratified Clinical Trials. J Biopharm Stat 28:292-308 |
| Psioda, Matthew A; Soukup, Mat; Ibrahim, Joseph G (2018) A practical Bayesian adaptive design incorporating data from historical controls. Stat Med 37:4054-4070 |
| Lawson, Michael T; Cho, Hunyong; Choudhury, Arkopal et al. (2018) Discussion of Laber et al. ""Optimal treatment allocations in space and time for on-line control of an emerging infectious disease"". J R Stat Soc Ser C Appl Stat 67:779-780 |
| Zhu, Anqi; Ibrahim, Joseph G; Love, Michael I (2018) Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics : |
| Liu, Yang; He, Qianchan; Sun, Wei (2018) Association analysis using somatic mutations. PLoS Genet 14:e1007746 |
| Pan, Yinghao; Cai, Jianwen; Longnecker, Matthew P et al. (2018) Secondary outcome analysis for data from an outcome-dependent sampling design. Stat Med 37:2321-2337 |
| Jung, Sin-Ho; Lee, Ho Yun; Chow, Shein-Chung (2018) Statistical Methods for Conditional Survival Analysis. J Biopharm Stat 28:927-938 |
| 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 : |
| Chung, Yunro; Ivanova, Anastasia; Hudgens, Michael G et al. (2018) Partial likelihood estimation of isotonic proportional hazards models. Biometrika 105:133-148 |
Showing the most recent 10 out of 549 publications
