The broad, long-term objectives of this research are the developments of statistical methods for the designs and analysis of clinical and epidemiological cancer studies, with or without genetic components.
The specific aims of this competing renewal application include: (1) exploring semiparametric linear transformation models for univariate and multivariate continuous response variables, (2) developing graphical and numerical techniques to assess model adequacy and predictive accuracy under semi- parametric transformation models for right censored failure time data, (3) studying semiparametric transformation models for the analysis of univariate and multivariate failure time data subject to interval censoring, (4) pursuing statistically efficient and computationally feasible procedures for the analysis of accelerated failure time and accelerated hazards models with right censored data, (5) investigating variance-components models for the joint linkage and association analysis of complex disease traits in family studies, (6) handling complex data structures (e.g., family data, selective genotyping, and correlated genetic and environmental factors with missing values) in the analysis of haplotype-disease associations, and (7) addressing the issue of population stratification in genetic association studies of unrelated individuals. All these problems are motivated by the principal investigator's applied research experiences and are highly relevant to current cancer research. The proposed solutions are based on likelihood and other sound statistical principles. The large-sample properties of the new estimators and test statistics will be established rigorously via modern empirical process theory and semiparametric efficiency theory. Efficient and reliable numerical algorithms will be developed to implement the inference procedures. The proposed methods will be evaluated extensively through computer simulation and be applied to a large number of cancer studies, most of which are carried out at the University of North Carolina. User-friendly software will be freely available to the general public. This research will not only significantly advance the fields of survival analysis, longitudinal data analysis and statistical genetics, but will also provide valuable new tools to cancer researchers.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Dunn, Michelle C
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University of North Carolina Chapel Hill
Biostatistics & Other Math Sci
Schools of Public Health
Chapel Hill
United States
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Li, Xiang; Xie, Shanghong; Zeng, Donglin et al. (2018) Efficient ?0 -norm feature selection based on augmented and penalized minimization. Stat Med 37:473-486
Wong, Kin Yau; Zeng, Donglin; Lin, D Y (2018) Efficient Estimation for Semiparametric Structural Equation Models With Censored Data. J Am Stat Assoc 113:893-905
Gao, Fei; Zeng, Donglin; Lin, Dan-Yu (2018) Semiparametric regression analysis of interval-censored data with informative dropout. Biometrics :
Tao, Ran; Zeng, Donglin; Lin, Dan-Yu (2017) Efficient Semiparametric Inference Under Two-Phase Sampling, With Applications to Genetic Association Studies. J Am Stat Assoc 112:1468-1476
Tang, Zheng-Zheng; Bunn, Paul; Tao, Ran et al. (2017) PreMeta: a tool to facilitate meta-analysis of rare-variant associations. BMC Genomics 18:160
Silva, Grace O; Siegel, Marni B; Mose, Lisle E et al. (2017) SynthEx: a synthetic-normal-based DNA sequencing tool for copy number alteration detection and tumor heterogeneity profiling. Genome Biol 18:66
Mao, Lu; Lin, D Y (2017) Efficient Estimation of Semiparametric Transformation Models for the Cumulative Incidence of Competing Risks. J R Stat Soc Series B Stat Methodol 79:573-587
Zeng, Donglin; Gao, Fei; Lin, D Y (2017) Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data. Biometrika 104:505-525
Mao, Lu; Lin, Dan-Yu; Zeng, Donglin (2017) Semiparametric regression analysis of interval-censored competing risks data. Biometrics 73:857-865
Lin, Dan-Yu; Dai, Luyan; Cheng, Gang et al. (2016) On confidence intervals for the hazard ratio in randomized clinical trials. Biometrics 72:1098-1102

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