The broad, long-term objectives of this research are to develop semiparametric regression models and associated inference procedures for the analysis of censored data, which are commonly encountered in medical studies.
The Specific Aims of this renewal application include: (1) development of a structural equation modelling frame- work with semiparametric failure time models for integrative analysis of multiple genomics platforms or other complex medical data, (2) investigation of semiparametric single-index regression models to discover optimal treatment regimens for potentially censored outcomes, (3) exploration of semiparametric random-effects models with time-varying regression coefficients to predict multiple types of recurrent adverse events and to assess the lag effects of drug exposures using post-approval surveillance data, and (4) pursuit of a counting-process modeling framework for individual-level infection time data with network information to understand disease dynamics at both individual and population levels. All these Aims address important new challenges arising from the latest medical research. The estimation of the proposed models is based on likelihood and other sound statistical principles. The large-sample properties of the estimators will be established rigorously through innovative use of modern empirical process theory and other advanced mathematical tools. Efficient and stable numerical algorithms will be developed to implement the inference procedures. The proposed methods will be evaluated extensively through simulation studies mimicking real data and be applied to our ongoing medical studies, including The Cancer Genome Atlas, Cancer and Leukemia Group B clinical trials, Observational Medical Outcomes Partnership databases, and HIV/STD prevention studies in North Carolina. Efficient, reliable and user-friendly open-source software with proper documentation will be produced. The proposed work will create new paradigms in survival analysis, advance medical research at UNC and elsewhere, and accelerate the search for effective strategies to prevent and treat cancers, cardiovascular diseases, AIDS, and other diseases of utmost public health importance.

Public Health Relevance

This research intends to tackle new challenges in the analysis of event time data from cutting-edge medical research, including genomics studies, personalized medicine, post-approval surveillance for drug safety, and disease networks. The proposed statistical paradigms will accelerate the search for effective strategies to prevent and treat cancers, cardiovascular disorders, AIDS, and other diseases of utmost public health importance.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM047845-28
Application #
9484276
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Brazhnik, Paul
Project Start
1992-08-01
Project End
2019-05-31
Budget Start
2018-06-01
Budget End
2019-05-31
Support Year
28
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Gao, Fei; Zeng, Donglin; Wei, Helen et al. (2018) Estimating Treatment Effects for Recurrent Events in the Presence of Rescue Medications: An Application to the Immune Thrombocytopenia Study. Stat Biosci 10:473-489
Jones, Sydney A; Li, Quefeng; Aiello, Allison E et al. (2018) Physical Activity, Sedentary Behavior, and Retirement: The Multi-Ethnic Study of Atherosclerosis. Am J Prev Med 54:786-794
Zeng, Donglin; Pan, Jean; Hu, Kuolung et al. (2018) Improving the power to establish clinical similarity in a Phase 3 efficacy trial by incorporating prior evidence of analytical and pharmacokinetic similarity. J Biopharm Stat 28:320-332
Wu, Jia-Rong; Cummings, Doyle M; Li, Quefeng et al. (2018) The effect of a practice-based multicomponent intervention that includes health coaching on medication adherence and blood pressure control in rural primary care. J Clin Hypertens (Greenwich) 20:757-764
Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang (2018) Chernoff Index for Cox Test of Separate Parametric Families. Ann Stat 46:1-29
Deng, Qiqi; Bai, Xiaofei; Liu, Dacheng et al. (2018) Power and sample size for dose-finding studies with survival endpoints under model uncertainty. Biometrics :
Kim, Soyoung; Zeng, Donglin; Cai, Jianwen (2018) Analysis of multiple survival events in generalized case-cohort designs. Biometrics :
Wang, Yuanjia; Fu, Haoda; Zeng, Donglin (2018) Learning Optimal Personalized Treatment Rules in Consideration of Benefit and Risk: with an Application to Treating Type 2 Diabetes Patients with Insulin Therapies. J Am Stat Assoc 113:1-13
Choi, Jaeun; Zeng, Donglin; Olshan, Andrew F et al. (2018) Joint modeling of survival time and longitudinal outcomes with flexible random effects. Lifetime Data Anal 24:126-152
Li, Quefeng; Cheng, Guang; Fan, Jianqing et al. (2018) Embracing the Blessing of Dimensionality in Factor Models. J Am Stat Assoc 113:380-389

Showing the most recent 10 out of 65 publications