Abstract
BackgroundQuantiles are a staple of epidemiologic research: in contemporary epidemiologic practice, continuous variables are typically categorized into tertiles, quartiles and quintiles as a means to illustrate the relationship between a continuous exposure and a binary outcome.DiscussionIn this paper we argue that this approach is highly problematic and present several potential alternatives. We also discuss the perceived drawbacks of these newer statistical methods and the possible reasons for their slow adoption by epidemiologists.SummaryThe use of quantiles is often inadequate for epidemiologic research with continuous variables.
Highlights
Quantiles are a staple of epidemiologic research: in contemporary epidemiologic practice, continuous variables are typically categorized into tertiles, quartiles and quintiles as a means to illustrate the relationship between a continuous exposure and a binary outcome
Epidemiology is often introduced using examples in which both exposure and outcome are considered in binary terms: research participants are defined as having, say, lung cancer or not, and being smokers or not, and the proportion of smokers compared between cases and controls
Analysis Categorization of continuously distributed exposure variables is associated with three problems: first, it involves multiple hypothesis testing with pairwise comparisons of quantiles; second, it requires an unrealistic step-function of risk that assumes homogeneity of risk within groups, leading to both a loss of power and inaccurate estimation; and third, it leads to difficulty comparing results across studies due to the data-driven cut points used to define categories
Summary
Analysis Categorization of continuously distributed exposure variables is associated with three problems: first, it involves multiple hypothesis testing with pairwise comparisons of quantiles; second, it requires an unrealistic step-function of risk that assumes homogeneity of risk within groups, leading to both a loss of power and inaccurate estimation; and third, it leads to difficulty comparing results across studies due to the data-driven cut points used to define categories. Meaningful comparisons derived from the non-linear model can concisely describe the association: we reported the difference in absolute risk of recurrence for a typical patient treated by a surgeon who had performed 10 procedures and for a surgeon who had performed 250 prior procedures These values were chosen after consultation with surgeons and were intended to reflect meaningful levels of experience; the estimates are obtained from the model including non-linear terms, not from a categorization approach. Analyses of continuous variables can be presented in readily meaningful terms; we would argue that clinically relevant comparisons are often more understood and useful than the estimates derived from data-driven quantiles Another argument against regression techniques involving non-linear terms is that the resulting models are prone to overfit [14]. Competing interests The author declare that they have no competing interests
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