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Correcting for bias in relative risk estimates due to exposure measurement error: a case study of occupational exposure to antineoplastics in pharmacists.

Authors
Spiegelman-D; Valanis-B
Source
Am J Publ Health 1998 Mar; 88(3):406-412
NIOSHTIC No.
20024390
Abstract
This paper describes 2 statistical methods designed to correct for bias from exposure measurement error in point and interval estimates of relative risk. The first method takes the usual point and interval estimates of the log relative risk obtained from logistic regression and corrects them for nondifferential measurement error using an exposure measurement error model estimated from validation data. The second, likelihood-based method fits an arbitrary measurement error model suitable for the data at hand and then derives the model for the outcome of interest. Data from Valanis and colleagues' study of the health effects of antineoplastics exposure among hospital pharmacists were used to estimate the prevalence ratio of fever in the previous 3 months from this exposure. For an interdecile increase in weekly number of drugs mixed, the prevalence ratio, adjusted for confounding, changed from 1.06 to 1.17 (95% confidence interval [CI] = 1.04, 1.26) after correction for exposure measurement error. Exposure measurement error is often an important source of bias in public health research. Methods are available to correct such biases.
Keywords
Epidemiology; Statistical-analysis; Risk-factors; Occupational-exposure; Antineoplastic-agents; Mathematical-models
Contact
Donna Spiegelman, ScD, Department of Epidemiology, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115
CODEN
AJHEAA
Publication Date
19980301
Document Type
Journal Article
Email Address
stdls@channing.harvard.edu
Funding Amount
161872
Funding Type
Grant
Fiscal Year
1998
NTIS Accession No.
NTIS Price
Identifying No.
Grant-Number-K01-OH-000106
Issue of Publication
3
ISSN
0090-0036
Priority Area
Grants Other
Source Name
American Journal of Public Health
State
MA
Performing Organization
Harvard University, School of Public Health, Department of Epidemiology and Biostatistics, Boston, Massachusetts
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