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.
|Potthoff, Richard F (2018) Differential losses to follow-up that are outcome-dependent can vitiate a clinical trial: Simulation results. J Biopharm Stat 28:633-644|
|Wang, Ting; Wang, Xiaofei; Zhou, Haibo et al. (2018) Auxiliary variable-enriched biomarker-stratified design. Stat Med 37:4610-4635|
|Burbank, Allison J; Todoric, Krista; Steele, Pamela et al. (2018) Age and African-American race impact the validity and reliability of the asthma control test in persistent asthmatics. Respir Res 19:152|
|Hibbard, Jonathan C; Friedstat, Jonathan S; Thomas, Sonia M et al. (2018) LIBERTI: A SMART study in plastic surgery. Clin Trials 15:286-293|
|Chen, Jingxiang; Fu, Haoda; He, Xuanyao et al. (2018) Estimating individualized treatment rules for ordinal treatments. Biometrics 74:924-933|
|Urrutia, Eugene; Chen, Hao; Zhou, Zilu et al. (2018) Integrative pipeline for profiling DNA copy number and inferring tumor phylogeny. Bioinformatics 34:2126-2128|
|Van den Berge, Koen; Perraudeau, Fanny; Soneson, Charlotte et al. (2018) Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications. Genome Biol 19:24|
|Luo, Yiwen; Maity, Arnab; Wu, Michael C et al. (2018) On the substructure controls in rare variant analysis: Principal components or variance components? Genet Epidemiol 42:276-287|
|Kong, Dehan; Maity, Arnab; Hsu, Fang-Chi et al. (2018) Rejoinder to ""A note on testing and estimation in marker-set association study using semiparametric quantile regression kernel machine"". Biometrics 74:767-768|
|Yu-Feng Liu, Leo; Liu, Yufeng; Zhu, Hongtu et al. (2018) SMAC: Spatial multi-category angle-based classifier for high-dimensional neuroimaging data. Neuroimage 175:230-245|
Showing the most recent 10 out of 549 publications