Objectives: The study seeks to asses the impact of measurement errors in cumulative exposure on estimates of a gene-environment interaction in a nested case-control study in occupational epidemiology. In the approach considered here, exposure intensity is assessed at the group level and the exposure duration individually (both with error). Genetic susceptibility is assumed to be known exactly. Differences in 'gene'are assumed to affect disease risk only in exposed subjects.
Methods: Three data analysis strategies were considered: one using a correctly specified disease model (exposure and exposure-gene interaction), and two using misspecified disease models, one with 'gene' as the only risk factor ('gene-only' model) and the other with main effects of both gene and exposure along with their interaction (‘full’ model).
Results: In simulations, estimates of the gene-environment interaction based on the correctly specified disease model were greatly attenuated and power was diminished appreciably even when errors in exposure were modest. Significant associations were detected more frequently in the gene-only model when errors in exposure were large. When the ‘full’ misspecified model was fitted to the simulated data, it yielded erratic estimates. This is illustrated an analysis of the interaction of cumulative exposure to organophosphate pesticides and paraoxonase gene on the risk of chronic neuropsychological effects among farmers who dip sheep.
Conclusion: If ‘gene’ contributes to disease risk only in presence of exposure, the existence of the gene-environment interaction can be efficiently inferred from a deliberately misspecified ‘gene-only’ disease model in nested case-control studies.
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