Accounting for uncertainty in systematic bias in exposure estimates used in relative risk regression
In many epidemiologic studies addressing exposure-response relationships, sources of error that lead to systematic bias in exposure measurements are known to be present, but there is uncertainty in the magnitude and nature of the bias. Two approaches that allow this uncertainty to be reflected in confidence limits and other statistical inferences were developed, and are applicable to both cohort and case-control studies. The first approach is based on a numerical approximation to the likelihood ratio statistic, and the second uses computer simulations based on the score statistic. These approaches were applied to data from a cohort study of workers at the Hanford site (1944-86) exposed occupationally to external radiation; to combined data on workers exposed at Hanford, Oak Ridge National Laboratory, and Rocky Flats Weapons plant; and to artificial data sets created to examine the effects of varying sample size and the magnitude of the risk estimate. For the worker data, sampling uncertainty dominated and accounting for uncertainty in systematic bias did not greatly modify confidence limits. However, with increased sample size, accounting for these uncertainties became more important, and is recommended when there is interest in comparing or combining results from different studies.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- National Inst. for Occupational Safety and Health, Rockville, MD (United States)
- DOE Contract Number:
- AC06-76RL01830
- OSTI ID:
- 195768
- Report Number(s):
- PNL-10909; ON: DE96004128
- Resource Relation:
- Other Information: PBD: Dec 1995
- Country of Publication:
- United States
- Language:
- English
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