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.
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.
|Wang, Zhi; Maity, Arnab; Luo, Yiwen et al. (2015) Complete effect-profile assessment in association studies with multiple genetic and multiple environmental factors. Genet Epidemiol 39:122-33|
|Geng, Yuan; Zhang, Hao Helen; Lu, Wenbin (2015) On optimal treatment regimes selection for mean survival time. Stat Med 34:1169-84|
|Liu, Yulun; Chen, Yong; Chu, Haitao (2015) A unification of models for meta-analysis of diagnostic accuracy studies without a gold standard. Biometrics 71:538-47|
|Chen, Qingxia; Zeng, Donglin; Ibrahim, Joseph G et al. (2015) Quantifying the average of the time-varying hazard ratio via a class of transformations. Lifetime Data Anal 21:259-79|
|Viele, Kert; Berry, Scott; Neuenschwander, Beat et al. (2014) Use of historical control data for assessing treatment effects in clinical trials. Pharm Stat 13:41-54|
|Wang, Xin; Zhang, Daowen; Tzeng, Jung-Ying (2014) Pathway-guided identification of gene-gene interactions. Ann Hum Genet 78:478-91|
|Chen, Ming-Hui; Ibrahim, Joseph G; Zeng, Donglin et al. (2014) Bayesian design of superiority clinical trials for recurrent events data with applications to bleeding and transfusion events in myelodyplastic syndrome. Biometrics 70:1003-13|
|Lin, Ja-An; Zhu, Hongtu; Mihye, Ahn et al. (2014) Functional-mixed effects models for candidate genetic mapping in imaging genetic studies. Genet Epidemiol 38:680-91|
|Zhang, Jing; Carlin, Bradley P; Neaton, James D et al. (2014) Network meta-analysis of randomized clinical trials: reporting the proper summaries. Clin Trials 11:246-62|
|Zeng, Donglin; Lin, D Y (2014) Efficient Estimation of Semiparametric Transformation Models for Two-Phase Cohort Studies. J Am Stat Assoc 109:371-383|
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