Introduction

Continuous variables (such as intraocular pressure (IOP), visual acuity, contrast sensitivity) are commonly measured in clinical ophthalmology and vision research. In clinical practice, a ‘status’ (category) can sometimes be assigned to an individual patient using a cutpoint in the value of a continuous variable; for example, a diagnosis of glaucoma might be confirmed by an elevated IOP measurement (eg, IOP >21 mm Hg). Indeed much of medicine revolves around an implicit classification of individuals into diseased and non-diseased. In clinical research, continuous variables may likewise be converted to categorical variables, assigning individuals to one of two groups. Although this may be appropriate for some specific studies where the underlying distribution of the variable shows a clear grouping, such dichotomisation has several drawbacks.1

Dichotomisation may be driven by the research question, for example, a study to investigate the health service needs of those with low vision, in which dichotomisation uses WHO visual acuity threshold for low vision.2 It may sometimes be used to bring the data in line with the clinical classification of patients but often the reason for dichotomisation of data is that it is thought to simplify the statistical analysis (eg, to enable use of a t test or a χ² test) and the presentation and interpretation of data. However, this simplification has a cost in terms of loss of information3 and may compromise the validity of the statistical analysis. We will discuss the disadvantages of dichotomisation and outline some points to consider before categorising continuous data. It should be noted that while the focus here is on categorisation of data into two groups, the problems arising when dichotomising data are inherent with any categorisation of data (two or more groups).