Abstract: The loss of signal associated with categorizing a continuous variable is well known, and previous studies have demonstrated that this can lead to an inflation of Type-I error when the categorized variable is a confounder in a regression analysis estimating the effect of an exposure on an outcome. However, it is not known how the Type-I error may vary under different circumstances, including logistic versus linear regression, different distributions of the confounder, and different categorization methods. Here we analytically quantified the effect of categorization, and then performed a series of 9600
Monte Carlo simulations to estimate the Type-I error inflation associated with categorization of a confounder under different regression scenarios. We show that Type-I
error is unacceptably high (>10% in most scenarios, and often 100%). The only exception
was when the variable categorized was a continuous mixture proxy for a genuinely
dichotomous latent variable, where both the continuous proxy and the categorized
variable are error-ridden proxies for the dichotomous latent variable. As expected, error
inflation was also higher with larger sample size, fewer categories, and stronger
associations between the confounder and the exposure or outcome. We provide online
tools that can help researchers estimate the potential error inflation and understand how
serious a problem this is
