Accounting for uncertainty in systematic bias in exposure estimates used in relative risk regression

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Description

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 ... continued below

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40 p.

Creation Information

Gilbert, E.S. December 1, 1995.

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  • Pacific Northwest Laboratory
    Publisher Info: Pacific Northwest Lab., Richland, WA (United States)
    Place of Publication: Richland, Washington

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Description

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.

Physical Description

40 p.

Notes

OSTI as DE96004128

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  • Other Information: PBD: Dec 1995

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  • Other: DE96004128
  • Report No.: PNL--10909
  • Grant Number: AC06-76RL01830
  • DOI: 10.2172/195768 | External Link
  • Office of Scientific & Technical Information Report Number: 195768
  • Archival Resource Key: ark:/67531/metadc673043

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  • December 1, 1995

Added to The UNT Digital Library

  • June 29, 2015, 9:42 p.m.

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  • April 7, 2016, 3:26 p.m.

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Gilbert, E.S. Accounting for uncertainty in systematic bias in exposure estimates used in relative risk regression, report, December 1, 1995; Richland, Washington. (digital.library.unt.edu/ark:/67531/metadc673043/: accessed December 15, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.