On quantifying the magnitude of confounding
When estimating the association between an exposure and outcome, a simple approach to quantifying the amount of confounding by a factor, Z , is to compare estimates of the exposure–outcome association with and without adjustment for Z . This approach is widely believed to be problematic due to the n...
| Published in: | Biostatistics |
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| Main Authors: | , , |
| Format: | Text |
| Language: | English |
| Published: |
Oxford University Press
2010
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| Subjects: | |
| Online Access: | http://biostatistics.oxfordjournals.org/cgi/content/short/11/3/572 https://doi.org/10.1093/biostatistics/kxq007 |
| Summary: | When estimating the association between an exposure and outcome, a simple approach to quantifying the amount of confounding by a factor, Z , is to compare estimates of the exposure–outcome association with and without adjustment for Z . This approach is widely believed to be problematic due to the nonlinearity of some exposure-effect measures. When the expected value of the outcome is modeled as a nonlinear function of the exposure, the adjusted and unadjusted exposure effects can differ even in the absence of confounding (Greenland , Robins, and Pearl, 1999); we call this the nonlinearity effect. In this paper, we propose a corrected measure of confounding that does not include the nonlinearity effect. The performances of the simple and corrected estimates of confounding are assessed in simulations and illustrated using a study of risk factors for low birth–weight infants. We conclude that the simple estimate of confounding is adequate or even preferred in settings where the nonlinearity effect is very small. In settings with a sizable nonlinearity effect, the corrected estimate of confounding has improved performance. |
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