Journal
STATISTICS IN MEDICINE
Volume 23, Issue 7, Pages 1159-1178Publisher
WILEY
DOI: 10.1002/sim.1687
Keywords
logistic regression; type I error rate; categorical variables; measurement error; confounding; epidemologic methods
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This paper demonstrates an inflation of the type I error rate that occurs when testing the statistical significance of a continuous risk factor after adjusting for a correlated continuous confounding variable that has been divided into a categorical variable. We used Monte Carlo simulation methods to assess the inflation of the type I error rate when testing the statistical significance of a risk factor after adjusting for a continuous confounding variable that has been divided into categories. We found that the inflation of the type I error rate increases with increasing sample size, as the correlation between the risk factor and the confounding variable increases, and with a decrease in the number of categories into which the confounder is divided. Even when the confounder is divided in a five-level categorical variable, the inflation of the type I error rate remained high when both the sample size and the correlation between the risk factor and the confounder were high. Copyright (C) 2004 John Wiley Sons, Ltd.
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