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Estimation with vanishing baseline risk.

Authors
Park-RM
Source
Epidemiology 2012 Nov; 23(6):937-938
NIOSHTIC No.
20041639
Abstract
Diseases associated with specific exposures may have little or no observable background rate in the absence of the exposure. Examples include mesothelioma (environmental asbestos), aplastic anemia (benzene), bronchiolitis obliterans (artificial butter flavorings), Reye's syndrome (aspirin in children), and angiosarcoma of the liver (vinyl chloride). Relative-rate models of exposure-response produce unstable near-zero baseline risk and unbounded coefficients, especially when age confounding requires baseline age dependence. The same problem arises in a proportional-hazards context. Baseline risk volatility also threatens meta-analyses, a procedure that assumes uniformity. Using Poisson regression, we investigated two methods: (1) fixing the intercept at a small value corresponding to 1% of attributable cases and (2) generating random sets of new cases across observation time independent of any predictor, possibly preempting true cases. Although models can be reliably fit using randomly generated cases, repetition would reduce variability in parameter estimates. The two treatments for vanishing baseline yield equivalent results demonstrating that simply fixing the intercept is entirely adequate.
Keywords
Risk-analysis; Risk-factors; Mathematical-models; Statistical-analysis; Statistical-quality-control; Exposure-assessment; Diseases; Analytical-processes
Contact
Robert M. Park, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health, Education and Information Division, 4676 Columbia Parkway, Cincinnati, OH 45226
CODEN
EPIDEY
Publication Date
20121101
Document Type
Other
Email Address
rhp9@cdc.gov
Fiscal Year
2013
NTIS Accession No.
NTIS Price
Identifying No.
B10242012
Issue of Publication
6
ISSN
1044-3983
NIOSH Division
EID
Priority Area
Construction; Mining
Source Name
Epidemiology
State
OH
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