I share your pessimism @f2harrell about the ability of high-dimensional data, electronic health record research, precision medicine, and so-called heterogeneity of treatment effect to provide useful clinical information. Traditionally, clinicians like me use diagnostic tests on their own to do this but admittedly with limited success. Simplistically, a test results within the normal range predicts that there is a low probability of an adverse disease outcome without treatment and it remains much the same if treatment is given. However, if it is outside the normal range, it is assumed that there will be a difference between the probability of the outcome on treatment and no treatment. However, the probabilities of an outcome on treatment and no treatment changes with the ‘severity’ of the test result and not in a ‘cliff edge’ within and outside the normal range.
It should be possible with care to take covariates into consideration possibly to increase the differences between the probabilities of an outcome with and without treatment. For example, if the albumin excretion rate (AER) is low (e.g. 20mcg/min) then it suggests that there is little renal glomerular damage and therefore little scope for improvement by treatment with an angiotensin receptor blocker (ARB). The probability of developing ‘nephropathy’ within 2 years is therefore about the same on control (e.g. 0.02) and treatment (e.g. 0.01) in figure 5 of a previous post: Should one derive risk difference from the odds ratio? - #340 by HuwLlewelyn . However, at an AER of 100mcg/min, the probability of nephropathy on control is 0.24 and on treatment it is 0.1, a risk difference of 0.14. In the RCT the covariates HbA1c and BP were kept to a minimum by treatment before randomisation so that the baseline risk was very low in the RCT. However, if there was poor diabetic control in an individual as evidenced by a high HbA1c, then this high risk should not be improved by treatment with an ARB as the latter does not improve diabetic control so that the risk reduction at an AER of 100mcg/min, would remain about 0.14. Another source of ‘heterogeneity of treatment effect’ would be the drug dosage of course, the expected difference being zero at a drug dosage of 0mg per day, increasing as the drug dosage is increased.
The above reasoning represents a hypothesis based on the results of a RCT. I agree therefore that it has to be tested by setting up calibration curves. I would be optimistic that a model based on the above type of reasoning based on a RCT result would provide helpful clinical predictions, unlike more speculative approaches.