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Activity Number:
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311
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Type:
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Contributed
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Date/Time:
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Wednesday, August 14, 2002 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Epidemiology*
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| Abstract - #301736 |
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Title:
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Cost-efficient Design of Main Cohort and Calibration Studies Where One or More Covariates are Measured with Errors
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Author(s):
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Sohee Park*+ and Daniel Stram
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Affiliation(s):
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University of Southern California and University of Southern California
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Address:
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1540 Alcazar St. CHP 220, Los Angeles, California, 90089-9011, USA
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Keywords:
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Nutritional epidemiology ; Calibration study ; Cohort study ; Study design ; Measurement error ; Correlated covariates
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Abstract:
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Calibration studies are often performed on a subgroup contained within or external to large studies, for the purpose of correcting risk estimates for the effect of measurement errors. In this paper, we present a method to optimally allocate the number of subjects in the main cohort and calibration studies by minimizing the total cost, while maintaining a fixed statistical power to detect a specified log relative risk. Measurement errors in the observed exposure are allowed to be subject to both random and systematic errors. We deal with the case when a gold standard is not available and repeated reference measures are obtained in calibration studies. It is shown that non-optimal choice of the number of replicates of reference measures per calibration study subject could result in a considerable waste of resources. Furthermore, the cost-efficient design is extended to a multivariate setting where covariates in the risk model are correlated. As the correlation between two covariates becomes stronger, the optimal sizes for both main cohort and calibration studies increase.
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