Knowledge and Data Management White Papers
Test for Linearity Between Continuous Confounder and Binary Outcome First, Run a Multivariate Regression Analysis Second
Overview Previous statistical studies have indicated that dichotomizing a continuous confounding variable in multivariate regression analyses can lead to biased estimation of the effect of exposures, treatments, and risk factors on outcomes. The paper suggests that, prior to entry in the multivariate analysis, one should test whether or not the continuous confounding variable is linearly related to log-odds of the binary outcome or hazard ratios of the time-to-event binary outcome. If there is a linear relationship, one encourages that the variable not be dichotomized. The paper illustrates this issue using clinical data that the author recently published in the New England Journal of Medicine (NEJM). The linearity assumption is tested by restricted cubic splines using SAS/Stat procedures.
| Publisher | SAS Institute | File Format | |
|---|---|---|---|
| Date Published | February 2009 | ||
| Format | White Papers | ||
| Topics | |||
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