The goal of this proposal is to develop statistical analysis methods for environmental health data when the health effects of interest are complex.
The Specific Aims are motivated by problems arising in toxicological studies and environmental epidemiological studies that investigate environmental effects that potentially result in multiple, interrelated changes over time. Empirical data analysis will play a central role in each of the Specific Aims. Environmental epidemiologists now routinely implement hierarchical study designs for an array o of biologic endpoints. A general class of hierarchical models for multilevel binary and ordinal symptom data will be developed, and the performance of these models will be compared to existing approaches known to be sensitive to modeling assumptions. Epidemiological studies have repeatedly shown associations between air particulate and increased morbidity and mortality in human populations, particularly in subjects with pre-existing respiratory or cardiac vulnerability. Current laboratory research focuses on the physiological mechanisms. Flexible models will be developed for assessing effects of exposure over time on multiple physiological endpoints.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Research Project (R01)
Project #
5R01ES012044-02
Application #
6889176
Study Section
Special Emphasis Panel (ZRG1-EPIC (03))
Program Officer
Gray, Kimberly A
Project Start
2004-05-01
Project End
2007-03-31
Budget Start
2005-04-01
Budget End
2006-03-31
Support Year
2
Fiscal Year
2005
Total Cost
$194,750
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
Lee, Kyu Ha; Dominici, Francesca; Schrag, Deborah et al. (2016) Hierarchical models for semi-competing risks data with application to quality of end-of-life care for pancreatic cancer. J Am Stat Assoc 111:1075-1095
Tabb, Loni Philip; Tchetgen, Eric J Tchetgen; Wellenius, Greg A et al. (2016) Marginalized zero-altered models for longitudinal count data. Stat Biosci 8:181-203
Bliznyuk, Nikolay; Paciorek, Christopher J; Schwartz, Joel et al. (2014) NONLINEAR PREDICTIVE LATENT PROCESS MODELS FOR INTEGRATING SPATIO-TEMPORAL EXPOSURE DATA FROM MULTIPLE SOURCES. Ann Appl Stat 8:1538-1560
Power, Melinda C; Weisskopf, Marc G; Alexeeff, Stacey E et al. (2013) Modification by hemochromatosis gene polymorphisms of the association between traffic-related air pollution and cognition in older men: a cohort study. Environ Health 12:16
Wilker, Elissa H; Mittleman, Murray A; Coull, Brent A et al. (2013) Long-term exposure to black carbon and carotid intima-media thickness: the normative aging study. Environ Health Perspect 121:1061-7
Kloog, Itai; Ridgway, Bill; Koutrakis, Petros et al. (2013) Long- and short-term exposure to PM2.5 and mortality: using novel exposure models. Epidemiology 24:555-61
Zigler, Corwin M; Dominici, Francesca; Wang, Yun (2012) Estimating causal effects of air quality regulations using principal stratification for spatially correlated multivariate intermediate outcomes. Biostatistics 13:289-302
Kloog, Itai; Melly, Steven J; Ridgway, William L et al. (2012) Using new satellite based exposure methods to study the association between pregnancy PM?.? exposure, premature birth and birth weight in Massachusetts. Environ Health 11:40
Hund, Lauren; Chen, Jarvis T; Krieger, Nancy et al. (2012) A geostatistical approach to large-scale disease mapping with temporal misalignment. Biometrics 68:849-58
Kloog, Itai; Coull, Brent A; Zanobetti, Antonella et al. (2012) Acute and chronic effects of particles on hospital admissions in New-England. PLoS One 7:e34664

Showing the most recent 10 out of 20 publications