Is the use of cutoffs for dichotomisation of biomarkers really that bad? Tissue microarrays are commonly used to assess potential prognostic biomarkers. For decades now, many authors (I would even say the majority) feel the need to categorise their continuous predictors, either by dichotomising after data collection, or by using scoring systems that pre-categorise (e.g. Allred and immunoreactive score).
The pitfalls of this approach are numerous and have been highlighted for years. Douglas Altman and Patrick Royston have been some of the most vocal critics, alongside prediction model experts like Frank Harrell.
I completely agree with their arguments, but does it really matter in practice? A frequent defence of dichotomisation, or categorisation of continuous predictors in general, is that it greatly simplifies clinical decision making. Indeed, a physician may have to weigh up what is essentially a binary choice – to treat or not to treat; or to present the patient with one prognosis or another – and so the belief is that predictors should be dichotomised to reflect this. I would argue that keeping a predictor continuous, however, allows for more granular information to be obtained and lessens the risk of misclassifying patients whose predictor values lie just above or below the arbitrary threshold.
However, countless clinical decisions are made off back of categorised predictors. For example, breast cancer patients are assessed for oestrogen, progesterone and HER2 expression status and are classified as receptor positive or negative as per some threshold. Although this approach may not be 'statistically sound', its widespread use in guiding decisions over treatment surely points to categorisation at least being somewhat useful?

It's really that bad, as detailed in many of Patrick and my writings.  Think about it these ways, for starters: If you categorize a marker the loss of information is so great that you will need to go an collect more markers to make up for the loss.  Why not instead get the most information out of a single marker?  Then there is the issue that it is easy to show that the threshold for a marker is a function of the continuous values of all the other predictors. See the Information Loss chapter in BBR.  For example, if the sex of the patient is an important factor with regard to the likelihood of disease, you'll find that a different biomarker threshold is needed for females vs. males.
Categorization simplifies decision making but only by making it worse.  Many things only seem to be simple precisely because they are wrong.
We are taught to assess goodness of fit of models.  A model that assumes a piecewise flat relationship between a biomarker and outcome will be easily shown to have poor fit to the data.  For example look at the log-likelihood for a piecewise flat model (i.e., one using thresholding) and a flexible but everywhere-smooth model.
