The proposed project is in response to PAR-12-197, Improving Diet and Physical Activity Assessment. We are primarily interested in developing and applying innovative statistical methods to assess or correct for measurement errors in physical activity and dietary intake data. We are motivated by physical activity measurement issues involved in the Nutrition and Exercise in Women (NEW) study, conducted from 2005 to 2009. The NEW study was a 12-month randomized, controlled trial using a 4-arm design to compare the effect of three lifestyle-change interventions (dietary weight loss, moderate-to-vigorous intensity aerobic exercise, or both interventions combined) versus control (no lifestyle change). Specific foci of this proposal include: (i) To develop and apply methods in the generalized linear model to adjust for measurement error in physical activity and dietary intake data when their quantiles are used as covariates. (ii) To develop and apply methods in survival analysis to adjust for measurement error in physical activity and dietary intake data when their quantiles are used as covariates. (iii) To develop and apply methods to adjust for measurement error in longitudinal physical activity and dietary intake data when replicates are not available. The proposed models and methods will be applied to the physical activity data of the NEW study, and the Women's Health Initiatives (WHI). Furthermore, the methods developed in the proposal will have general applications to other studies of physical activity and dietary intake data.

Public Health Relevance

The proposed project is to develop and apply innovative statistical methods to assess or correct for measurement errors in the physical activity and dietary intake data. The proposed models and methods will be applied to the physical activity data of the NEW study, and the WHI. Furthermore, the methods developed in the proposal will have general applications to other studies of physical activity and dietary intake data.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21HL121347-02
Application #
8787793
Study Section
Kidney, Nutrition, Obesity and Diabetes (KNOD)
Program Officer
Boyington, Josephine
Project Start
2014-01-01
Project End
2015-12-31
Budget Start
2015-01-01
Budget End
2015-12-31
Support Year
2
Fiscal Year
2015
Total Cost
$235,263
Indirect Cost
$93,452
Name
Fred Hutchinson Cancer Research Center
Department
Type
DUNS #
078200995
City
Seattle
State
WA
Country
United States
Zip Code
98109
Huang, Yijian; Wang, Ching-Yun (2018) Cox regression with dependent error in covariates. Biometrics 74:118-126
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
Wang, Ching-Yun; Cullings, Harry; Song, Xiao et al. (2017) Joint nonparametric correction estimator for excess relative risk regression in survival analysis with exposure measurement error. J R Stat Soc Series B Stat Methodol 79:1583-1599
Wang, Zhu; Ma, Shuangge; Zappitelli, Michael et al. (2016) Penalized count data regression with application to hospital stay after pediatric cardiac surgery. Stat Methods Med Res 25:2685-2703
Wang, Ching-Yun; Song, Xiao (2016) Robust best linear estimator for Cox regression with instrumental variables in whole cohort and surrogates with additive measurement error in calibration sample. Biom J 58:1465-1484
Yu, Hsiang; Cheng, Yu-Jen; Wang, Ching-Yun (2016) Semiparametric Regression Estimation for Recurrent Event Data with Errors in Covariates under Informative Censoring. Int J Biostat 12:
Xu, Yuhang; Li, Yehua; Song, Xiao (2016) Locally Efficient Semiparametric Estimators for Proportional Hazards Models with Measurement Error. Scand Stat Theory Appl 43:558-572
Wang, Ching-Yun; Tapsoba, Jean De Dieu; Duggan, Catherine et al. (2016) Methods to adjust for misclassification in the quantiles for the generalized linear model with measurement error in continuous exposures. Stat Med 35:1676-88
Dai, James Y; Zhang, Xinyi Cindy; Wang, Ching-Yun et al. (2016) Augmented case-only designs for randomized clinical trials with failure time endpoints. Biometrics 72:30-8
Wang, Zhu; Ma, Shuangge; Wang, Ching-Yun (2015) Variable selection for zero-inflated and overdispersed data with application to health care demand in Germany. Biom J 57:867-84

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