This proposal addresses statistical issues arising from measurement error problems, with an emphasis on modeling within a Bayesian framework. Measurement error arises when a variable of interest, such as exposure to a contaminant, cannot be measured or is too expensive to measure for most subjects. Instead inference must based on a proxy variable measured with error, which can lead to incorrect conclusions if not properly addressed. This proposal is motivated by applications in environmental health studies, in which measurement error is often a problem.
The specific aims are: (1) To delineate the advantages and disadvantages of Bayesian and frequentist approaches to measurement error problems using a case study approach; (2) Within a Bayesian framework, to examine the sensitivity of numerical estimates of the relationship between the outcome and covariates to their assumed distributions; (3) To develop a Bayesian paradigm for the 2-stage case- control design, and to compare numerical estimates with those from frequentist methods; and (4) To develop efficient sampling strategies for measuring the """"""""gold standard"""""""" in a validation sub-study within a repeated measures setting. The research will be illustrated and applied to several datasets collected for environmental health studies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Environmental Health Sciences (NIEHS)
Type
Postdoctoral Individual National Research Service Award (F32)
Project #
1F32ES005834-01
Application #
2638407
Study Section
Special Emphasis Panel (ZRG4-EDC-1 (01))
Program Officer
Lohrey, Nancy
Project Start
1998-11-01
Project End
Budget Start
1998-05-01
Budget End
1999-04-30
Support Year
1
Fiscal Year
1998
Total Cost
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
082359691
City
Boston
State
MA
Country
United States
Zip Code
02115