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Inflation of the type I error rate when a continuous confounding variable is categorized in logistic regression analyses
Author(s) -
Austin Peter C.,
Brunner Lawrence J.
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1687
Subject(s) - confounding , statistics , categorical variable , type i and type ii errors , logistic regression , mathematics , inflation (cosmology) , sample size determination , econometrics , variable (mathematics) , regression analysis , physics , mathematical analysis , theoretical physics
Abstract 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 2004 John Wiley & Sons, Ltd.