In defence of the dichotomy

One of the most important ways in which we learn to understand the world is through the use of simple categories to describe more complicated phenomena. For example, hot and cold, red and green, sweet and sour, wet and dry, ill and well, hypertensive and normotensive, and diabetic and non-diabetic are all simple classifications that allow us to describe our perceptions, or those of others. Sometimes the categories appear, on the surface, to reflect a simple dichotomy of an underlying scale; in other cases, a number of underlying factors appear to be involved, or there may be more than two categories. But whatever their basis, their use allows us to describe our perceptions first to ourselves, permitting the development of our own understanding, and secondly to others, facilitating communication. Under these circumstances it is hardly surprising that medical researchers often seek to categorize data in their attempt to make sense of unfamiliar measurement scales and treatment effects of uncertain implication. After all, this strategy has proved highly effective in medicine in the past. A raised temperature (over 378C) has become one trigger for the treatment of infections, and there are many other similar thresholds, for example those for the treatment of raised blood pressure, plasma cholesterol or blood sugar. Even in less measurable psychiatric disorders such as depression, patients are labelled as depressed and seriously depressed on the basis of clinical assessment, often including the use of one or more rating scales. And in all these diseases, the treating physician watches out for a subsequent return to ‘normal’, that is, a ‘response’. In his recent Viewpoint article in this journal [1], Stephen Senn decries the use of ‘responder analysis’ in controlled clinical trials. This entails classifying patients as ‘responders’ or non-responders’ on the basis of their outcome measurements and then analysing the resulting dichotomous data. As usual, Senn’s article is both entertaining and educative. Unusually, however, his main messages are wrong. Take his concluding sentences: ‘The net consequence of dichotomizing continuous data is that trials are much bigger than they need be, that our inferences are poorer and that we are wasting both resources and lives. Thinking about appropriate measurements is an important part of any science. Physicists take it very seriously. Trialists should do the same. ’ All the critical aspects of this extract misstate (or, at the very least, grossly overstate) the true situation. Starting at the end of this paragraph, let me take umbrage on behalf of the many trialists with whom I have worked, and the many trialists whose work I have reviewed, the majority of whom regard the choice of primary and secondary measurements as a key aspect of trial design. It is hard to think of a subject that gets more pre-trial discussion, both within sponsor companies and externally with trialists and regulatory authorities. Nearly all disease-specific regulatory guidelines focus on the topic, and most general methodological guidelines also address it. No, it is not lack of serious consideration that leads to dichotomizing data and the use of responder analysis. Nor is it a devious ploy to