The broad, long-term objectives of this research are the developments of innovative and high-impact statistical methods for the designs and analysis of chronic disease studies, with an emphasis on genomics.
The specific aims of this competing renewal application include: (1) efficient estimation for general two-phase studies, in which possibly incomplete multivariate outcomes and inexpensive covariates are measured on all study subjects in the first phase and the first-phase information is used to optimally select subjects for measurements of expensive covariates in the second phase;(2) valid and efficient analysis of genetic association when all study subjects are genotyped on a SNP array but only a small subset is sequenced or when unobserved allele-specific copy numbers are of direct interest;(3) meta-analysis under random-effects models when the number of studies is small relative to study sample sizes and variable selection based on summary statistics of multiple studies under a variety of model structures. All these problems are motivated by the principal investigator's applied research experiences and are highly relevant to current genomic studies. The proposed solutions are based on likelihood and other sound statistical principles. The large-sample properties of the new methods will be established rigorously via modern empirical process theory and semiparametric efficiency theory. 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 several major genomic studies, most of which are carried out at the UNC. Efficient, reliable and user-friendly software with proper documentation will be freely available. This research will not only advance the fields of biostatistics and statistical genetics but also influence chronic disease research at the UNC and elsewhere.
The broad, long-term objectives of this research are the developments of innovative and high-impact statistical methods for the designs and analysis of chronic disease studies, with an emphasis on genomics. The specific aims of this competing renewal application include efficient estimation under outcome-dependent sampling, genetic association analysis with incomplete DNA data, and meta-analysis with heterogeneous effects and high-dimensional covariate data. This research will not only advance the fields of biostatistics and statistical genetics but also influence current chronic disease research.
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|Hu, Wenrong; Cai, Jianwen; Zeng, Donglin (2014) Sample size/power calculation for stratified case-cohort design. Stat Med 33:3973-85|
|Lin, Dan-Yu; Tao, Ran; Kalsbeek, William D et al. (2014) Genetic association analysis under complex survey sampling: the Hispanic Community Health Study/Study of Latinos. Am J Hum Genet 95:675-88|
|Zeng, Donglin; Ibrahim, Joseph G; Chen, Ming-Hui et al. (2014) Multivariate recurrent events in the presence of multivariate informative censoring with applications to bleeding and transfusion events in myelodysplastic syndrome. J Biopharm Stat 24:429-42|
|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|
|Tang, Zheng-Zheng; Lin, Dan-Yu (2014) Meta-analysis of sequencing studies with heterogeneous genetic associations. Genet Epidemiol 38:389-401|
|Chen, Tianle; Wang, Yuanjia; Chen, Huaihou et al. (2014) Targeted Local Support Vector Machine for Age-Dependent Classification. J Am Stat Assoc 109:1174-1187|
|Zhou, Haibo; Xu, Wangli; Zeng, Donglin et al. (2014) Semiparametric Inference for Data with a Continuous Outcome from a Two-Phase Probability Dependent Sampling Scheme. J R Stat Soc Series B Stat Methodol 76:197-215|
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|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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