Determining the Probability Distribution and Evaluating Sensitivity and False Positive Rate of a Confounder Detection Method Applied To Logistic RegressionRobin Bliss1,2, Janice Weinberg3, Thomas Webster1 and Veronica Vieira1,4*
- *Corresponding Author:
- Veronica Vieira
University of California Irvine,
Email: [email protected]
Received date: May 02, 2012; Accepted date: May 21, 2012; Published date: May 23, 2012
Citation: Bliss R, Weinberg J, Webster T, Vieira V (2012) Determining the Probability Distribution and Evaluating Sensitivity and False Positive Rate of a Confounder Detection Method Applied To Logistic Regression. J Biomet Biostat 3:142. doi:10.4172/2155-6180.1000142
Copyright: © 2012 Bliss R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Background: In epidemiologic studies researchers are often interested in detecting confounding (when a third variable is both associated with and affects associations between the outcome and predictors). Confounder detection methods often compare regression coefficients obtained from “crude” models that exclude the possible confounder(s) and “adjusted” models that include the variable(s). One such method compares the relative difference in effect estimates to a cutoff of 10% with differences of at least 10% providing evidence of confounding.
Methods: In this study we derive the asymptotic distribution of the relative change in effect statistic applied to logistic regression and evaluate the sensitivity and false positive rate of the 10% cutoff method using the asymptotic distribution. We then verify the results using simulated data.
Results: When applied to a logistic regression models with a dichotomous outcome, exposure, and possible confounder, we found the 10% cutoff method to have an asymptotic lognormal distribution. For sample sizes of at least 300 the authors found that when confounding existed, over 80% of models had >10% changes in odds ratios. When the confounder was not associated with the outcome, the false positive rate increased as the strength of the association between the predictor and confounder increased. When the confounder and predictor were independent of one another, false positives were rare (most < 10%).
Conclusions: Researchers must be aware of high false positive rates when applying change in estimate confounder detection methods to data where the exposure is associated with possible confounder variables.