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Abstract #167215

Holly James, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N. PO Box 19024, Seattle, WA 98109, (206) 288-1024, hutchdoc@fhcrc.org

In studying the association between an exposure and an 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 recognized as flawed due to a phenomenon called non-collapsibility. With a non-linear measure of association, there can be a difference between the adjusted and unadjusted associations even in the absence of confounding.

We explore a new approach to quantifying confounding which separates confounding bias from non-collapsibility bias. The relative performances of the two approaches to quantifying confounding are assessed in simulations and in a data example, where we quantify confounding due to age in the smoking-lung cancer association. We conclude that the simple approach to quantifying confounding is adequate in most settings.

Learning Objectives:

• none available.

Presenting author's disclosure statement:

Any relevant financial relationships? No
Any institutionally-contracted trials related to this submission?

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.