Researchers often take variables that are measured on a continuum and then break them into categories (for example, above or below some cut-point), either to place subjects into groups or as an outcome measure. In this article, we show that the rationales given for this practice are weak and that categorization results in lost information, reduced power of statistical tests, and increased proba...

Specific morphotypic profiles of normal and abnormal vaginal flora, including bacterial vaginosis (BV), were characterized. A prospective study of 350 women yielded concurrent Gram-stain data and clinical assessment (n = 3455 visits). Microbiological profiles were constructed by Gram stain. Eight profile definitions were based on dichotomizing the levels of Lactobacillus, Gardnerella, and curve...

In this paper, we study the statistical issues related to omission and categorization of important covariates in context Fine–Gray model randomized-controlled trials with competing risks. We show that an covariate from leads attenuated estimates for treatment effect loss proportionality general. Our simulation studies reveal substantial attenuation estimate power, while dichotomizing a continuo...

Objectives Dichotomizing continuous data prior to analysis is rarely a good idea as power is lost. However expressing results as a difference of means is not always meaningful for clinicians; small differences in means may seem clinically unimportant while the same study results expressed as a difference/ratio of proportions or number needed to treat might reveal an effect that is considered wo...