Erin
I will try to show how government financial support could be used to make medicine more ‘personalised’ by improving the diagnostic process.
The medical profession has already been practicing ‘personalised medicine’ down the ages by arriving at diagnoses. I will show that Pearl and Muller have been trying to re-invent this wheel and basically getting a similar result using a very implausible example that made their reasoning difficult to follow. They claimed that they were addressing the outcome for a specific individual (hence @Stephen 's title of this topic being ‘Individual response’). However, what they were actually doing was dealing with a smaller subpopulation of the RCT subpopulation resembling that individual much more closely. We would describe these smaller subpopulations based on covariates as diagnostic categories.
The purpose of assessing individual response is to choose the plan of most benefit to the individual patient. Turning the clock back to create a ‘counterfactual’ situation and comparing the effect of treatment and placebo by doing this many times can be simulated with an N-of-1 RCT. The process of randomisation does the same job as a time machine. Comparing treatment counterfactually between groups of individuals using a time machine can be simulated with a standard RCT.
Choosing a test as a covariate based on a sound hypothesis before randomisation is basically to create a diagnostic criterion. We may thus create a positive test result that acts as a sufficient criterion that confirms a diagnosis If the latter is also a necessary criterion and therefore a definitive criterion, then a negative covariate test acts as a criterion that excludes a diagnosis. Those with a positive diagnosis are expected to respond better to treatment than those testing negative (the latter usually showing ‘insignificant’ or no improvement because they do not have the ‘disease’ usually). The degree of difference in the probability of the outcome with or without treatment reflects the validity of the test as a diagnostic criterion.
Pearl and Muller created an implausible diagnostic category not based on any reasonable medical hypothesis. Instead their positive diagnostic test was based on the patient ‘feeling positive’ about a treatment (and inclined to accept it if allowed to choose). A negative diagnostic test was those ‘feeling negative’ about a drug based on a negative feeling and inclined to reject it if allowed to choose). There was no plausible medical hypothesis for the potential utility of this ‘test’. However I shall refer to them as positive and negative test results to show how they behave as diagnostic criteria.
In their imaginary RCT on men, 49% of the total had a positive test and survived on treatment, whereas 0% of the total had a positive test and survived on placebo, showing a simulated counterfactual ‘Benefit’ of 49-0 = 49%. However, 0% had a negative test and survived on treatment and 21% had a negative test and survived on placebo showing a simulated counterfactual ‘Harm’ of 21-0 = 21%.
From this information we would advise a man with future positive result to accept treatment resulting in a survival of 49% but the negative testing man to refuse it resulting in a survival’ of 21% on placebo. By advising this for men in a subsequent observational study, 21% + 49% = 70% would survive.
In their imaginary RCT on females, 18.9% of the total had a positive test and survived on treatment, whereas 0% had a positive test and survived on placebo giving a ‘Benefit’ 18.9-0 = 18.9%. Moreover, 30% had a negative test result and survived on treatment and 21% had a negative test result and survived on placebo also giving ‘Benefit’ of 30-21 = 9%. Neither a positive nor negative result gave an outcome where placebo was better than treatment, hence ‘Harm’ in females was 0%. This gives a total ‘Benefit’ for females of 18.9+9 = 27.9%.
From this information we would advise a woman to accept a treatment whether she felt positive (giving 18.9% survival) or negative (giving 30% survival). By advising this for women in a subsequent observational study, 18.9 + 30 = 49.9% would survive. These figures are readily available from my Bayes P Map in post 224. (https://discourse.datamethods.org/t/individual-response/5191/224?u=huwllewelyn ) The P map representing the application of Bayes rule to the RCT data therefore confirms what Pearl and Muller found by using their mathematics.
I should add that many experienced physicians, especially endocrinologists, do not dichotomise test results into positive and negative, but interpret intuitively each numerical value on a continuous scale from very low to very high. This is done for the personal numerical result of each patient. There would be two such curves, one for patients on placebo and another for those on treatment (see Figure 1 in my post on ‘Risk based treatment and the validity of scales of effect’ https://discourse.datamethods.org/t/risk-based-treatment-and-the-validity-of-scales-of-effect/6649?u=huwllewelyn ).
At low / normal values the probability of an adverse outcome on placebo would be near zero with the treatment and placebo curves curve superimposed. This would provide near certainty in avoiding treatment as of no benefit for the purpose of personalised medicine. The remaining parts of the curve (see Figure 1 in my post on ‘Risk based treatment and the validity of scales of effect’ https://discourse.datamethods.org/t/risk-based-treatment-and-the-validity-of-scales-of-effect/6649?u=huwllewelyn ) would provide more personalised probabilities of outcome on treatment and control for each patient depending on the precise numerical result of a test than probabilities conditional on positive or negative results that lump together wide ranges of numerical results. If these curves were plotted for degrees of positivity / negativity from the example of Pearl and Muller, the curves would cross over implausibly at some the point.
Maybe the way forward for personalised medicine is for governments to support expert statistical input and other resources to help us clinicians to develop these curves and related concepts.