Analysis by Categorizing or Dichotomizing Continuous Variables Is Inadvisable: An Example from the Natural History of Unruptured Aneurysms

Abstract

SUMMARY:In medical research analyses, continuous variables are often converted into categoric variables by grouping values into Ն2 categories. The simplicity achieved by creating Ն2 artificial groups has a cost: Grouping may create rather than avoid problems. In particular, dichotomization leads to a considerable loss of power and incomplete correction for confounding factors. The use of data-derived "optimal" cut-points can lead to serious bias and should at least be tested on independent observations to assess their validity. Both problems are illustrated by the way the results of a registry on unruptured intracranial aneurysms are commonly used. Extreme caution should restrict the application of such results to clinical decision-making. Categorization of continuous data, especially dichotomization, is unnecessary for statistical analysis. Continuous explanatory variables should be left alone in statistical models.ABBREVIATIONS: ACA ϭ anterior cerebral artery; CHUM ϭ Centre hospitalier de l'Université de Montré al; ICA ϭ internal carotid artery; ISUIA ϭ International Study of Unruptured Intracranial Aneurysms; MCA ϭ middle cerebral artery; Pcirc ϭ posterior circulation; PcomA ϭ posterior communicating artery; SAH ϭ subarachnoid hemorrhage; UIA ϭ unruptured intracranial aneurysms U IAs are common (approximately 2% of the adult population), but they most often remain silent until a rupture occurs (incidence, 2-20/10,000/year).1 No one is sure what to do with them, but with the increasing accessibility of noninvasive imaging of the brain, the problem is growing rapidly. 2A common and yet controversial approach to decisionmaking is to compare the natural history of the disease and the risks of treatment.3,4 One prominent risk factor for rupture of UIAs is size. In 1998, a landmark study on this subject, the ISUIA, estimated from retrospectively obtained data that the risk of rupture of aneurysms smaller than 10 mm was extremely low.5 Subsequent guidelines published in 2000 discouraged the treatment of aneurysms smaller than that size. 6In a 2003 study, the same group, confronted with different results when data were collected prospectively, claimed that aneurysms Ͻ7 mm in a special subgroup of patients (defined by the absence of a history of rupture of another lesion, having an aneurysm located in the anterior circulation, and selected for observation) were at zero risk of rupture, but only when some carotid aneurysms were excluded (PcomA aneurysms). 7Despite these specifications, a threshold of 7 mm is now used by many as a normative criterion for clinical decisions 8 or in cost-effectiveness analyses. 9Size of UIAs can serve to illustrate the problems associated with the categorization of continuous variables, in particular dichotomization. Our aim is to consider how continuous variables should be treated and analyzed when we suspect that risk increases or decreases in proportion to the variable in question. We address the following questions: What are the advantages and disadvantages of categorization? If we de...

Self-perceived Mental Health Status and Uptake of Fecal Occult Blood Test for Colorectal Cancer Screening in Canada: A Cross-Sectional Study

“…17,31,32 Dichotomization makes analysis less cumbersome, but may introduce bias in the study result (eg, misclassification). 33 The current study made use of all information and demonstrated that individuals with lower SPMH (poor or fair) were less likely to have ever had FOBT compared to those with higher perceived mental health (very good or excellent). Looking on up to date screening, however, the difference in FOBT uptake based on one's perceived mental health was not significant.…”

Examining the association between C-Reactive protein and obesity by using the fractional polynomial approach; applying on NHANES dataset from 2001 to 2010

“…Using the WHO categorization for obesity, the categorical model provided the least precise predicted values of ln(CRP) level [7,23]. Making full use of the scale data for BMI, the linear…”

“…In the male sub-sample, the otherHispanic group displayed the highest curve when BMI>30. We also found differences in the shape of BMI-ln(CRP) curve and ln(CRP) estimates compared to other models for women and men sub-samples.Using the WHO categorization for obesity, the categorical model provided the least precise predicted values of ln(CRP) level [7,23]. Making full use of the scale data for BMI, the linear model demonstrated advantages over the categorical model.…”

The Problem of Subgroup Analyses: An Example from a Trial on Ruptured Intracranial Aneurysms

“…The situation may be further complicated by the use of dichotomization or categorization of continuous variables according to various cutoff values (such as age categorized in decades 2,9 or as Ͼ65 years or Ͻ65 years), 3 a process that allows the production of various, sometimes diverging, results. 25 The reluctance of investigators to acknowledge that a specific hypothesis was post hoc can prevent readers from being cautious in interpreting these findings. These problems can only be prevented by requiring precise specification and publication of detailed protocols of clinical trials.…”