This continuing project aims to develop and evaluate improved statistical methods for the design and analysis of epidemiologic studies. There will be a substantial emphasis on methods development for genetic epidemiologic studies in the proposed grant period. A multistage design that incorporates aggregation, segregation, linkage and candidate gene analysis will be developed. Properties of such a design will be studied numerically using likelihood-based procedures. Estimating equation-based methods will be developed for data arising from such a design, including emphasis on both discrete/continuous outcomes and censored failure time outcomes. Frailty methods will also be developed for the failure time analysis of epidemiologic data on families and will be compared with the estimating equation methods. There will also be a continuing emphasis on traditional cohort and case- control studies of identified risk factors. This work will include the development and evaluation of methods for nonparametric relative risk estimation. It will also include an emphasis on covariate measurement error methods development, including the relaxation of classical normal theory-based measurement methods to allow the measurement error distribution to depend on selected study subject characteristics, and the further development of less parametric procedures for use with a validation sample, or a partial validation sample. Finally this project will continue the development of aggregate data (ecologic) study methods, heretofore conducted under Project 4. In particular, design and analysis aspects of a study procedure that combines disease rates from several -geographic areas with exposure and confounding factor data from randomly selected persons in each area, will continue to be developed, as will related aspects of a procedure involving one or more time series of disease rates. Collectively this work can be expected to generate new design and analysis procedures that will improve the efficiency and reliability of a range of important types of epidemiologic studies.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Program Projects (P01)
Project #
2P01CA053996-21
Application #
6269469
Study Section
Project Start
1998-01-01
Project End
1998-12-31
Budget Start
1997-10-01
Budget End
1998-09-30
Support Year
21
Fiscal Year
1998
Total Cost
Indirect Cost
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
075524595
City
Seattle
State
WA
Country
United States
Zip Code
98109
Monaco, John V; Gorfine, Malka; Hsu, Li (2018) General Semiparametric Shared Frailty Model: Estimation and Simulation with frailtySurv. J Stat Softw 86:
Dai, James Y; Wang, Xiaoyu; Buas, Matthew F et al. (2018) Whole-genome sequencing of esophageal adenocarcinoma in Chinese patients reveals distinct mutational signatures and genomic alterations. Commun Biol 1:174
Dai, James Y; Peters, Ulrike; Wang, Xiaoyu et al. (2018) Diagnostics for Pleiotropy in Mendelian Randomization Studies: Global and Individual Tests for Direct Effects. Am J Epidemiol 187:2672-2680
Dai, James Y; Liang, C Jason; LeBlanc, Michael et al. (2018) Case-only approach to identifying markers predicting treatment effects on the relative risk scale. Biometrics 74:753-763
Prentice, Ross L; Zhao, Shanshan (2018) Nonparametric estimation of the multivariate survivor function: the multivariate Kaplan-Meier estimator. Lifetime Data Anal 24:3-27
Howard, Barbara V; Aragaki, Aaron K; Tinker, Lesley F et al. (2018) A Low-Fat Dietary Pattern and Diabetes: A Secondary Analysis From the Women's Health Initiative Dietary Modification Trial. Diabetes Care 41:680-687
Huang, Yijian; Wang, Ching-Yun (2018) Cox regression with dependent error in covariates. Biometrics 74:118-126
Su, Yu-Ru; Di, Chongzhi; Bien, Stephanie et al. (2018) A Mixed-Effects Model for Powerful Association Tests in Integrative Functional Genomics. Am J Hum Genet 102:904-919
Liu, Dandan; Cai, Tianxi; Lok, Anna et al. (2018) Nonparametric Maximum Likelihood Estimators of Time-Dependent Accuracy Measures for Survival Outcome Under Two-Stage Sampling Designs. J Am Stat Assoc 113:882-892
Yu, Hsiang; Cheng, Yu-Jen; Wang, Ching-Yun (2018) Methods for multivariate recurrent event data with measurement error and informative censoring. Biometrics 74:966-976

Showing the most recent 10 out of 319 publications