The broad, long-term objectives of this research are the development of novel and high-impact statistical and computational tools for discovering genetic variants associated with inter-individual differences in the efficacy and toxicity of cancer medications and for optimizing drug therapy on the basis of each patient's genetic constitution.
The specific aims i nclude: (1) construction of robust and efficient statistical methods for assessing the effects of SNP genotypes and haplotypes on drug response with a variety of phenotypes (e.g., binary and continuous measures of efficacy and toxicity, right-censored survival time, interval-censored time tp disease progression, and informatively censored PSA levels and adverse events);(2) development of statistical and data-mining techniques for predicting drug response based on high-dimensional, highly correlated genomic data and complex phenotypes;(3) investigation of statistical procedures for providing low-bias estimation of effect sizes with complex and highly multivariate genetic data for follow-up and confirmation studies;(4) exploration of a new form of machine learning for identifying candidate individualized therapies in both pre-clinical studies and clinical trials. All these aims have been motivated by the investigators'applied research experiences and address the most timely and important issues in pharmacogenomics and individualized therapy. The proposed solutions are built on sound statistical and data-mining principles. The theoretical properties of the new methods will be established rigorously via modern empirical process theory and other advanced mathematical arguments. Efficient and stable numerical algorithms will be devised to implement the new methods. Extensive simulation studies will be conducted to evaluate the operating characteristics of the new inferential and numerical procedures in realistic settings. Applications will be provided to a large number of cancer studies, most of which are carried out at Duke University and the University of North Carolina at Chapel Hill. Practical and user-friendly software will be developed and disseminated freely to the general public. Our research will change the ways pharmacogenomic studies and individualized therapy trials are designed and analyzed, which will lead to optimal treatments for patients in cancer and other diseases.
The proposed research will develop new statistical methods that will significantly improve the ways pharmacogenomic studies and individualized therapy trials are designed and analyzed. This will improve public health by hastening the discovery of better treatments for patients in cancer and in other diseases.
|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