Individual response

data analysis
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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.

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Hi Huw

I appreciate your faith that the proposals in this paper will, at some point, permeate my thick skull if they are explained in different ways. Unfortunately, I think my brain resists attempts to make sense of arguments that hinge on wildly implausible medical/physiologic/behavioural premises and assumptions. It rebels against the futility of the whole exercise. Arguing that patient preference could serve as a reliable predictor of patient “responsiveness” to a treatment feels like a very deus ex machina way to overcome the barriers to personalized medicine. But it’s also very possible that I’m just not capable of thinking deeply enough about all this…

Since physicians have to make many treatment-related decisions every day, our decision-making framework needs to be compatible with rapid decision-making. I’m not saying that we won’t someday be able to fine-tune how we choose therapies for patients. Rather, I’m pointing out that if the process of “fine-tuning” takes an hour, it will never, realistically, be implemented. In that world, physicians would only be able to see two patients per day.

I see a lot more opportunity for “personalization” in fields where therapies have a combination of modest efficacy but guaranteed toxicity. Risk/benefit assessments can be challenging in these scenarios, so it’s important to elicit patients’ values and preferences (“utilities”) before recommending a particular course of action. But decisions of this complexity are not the norm in medicine. Instead, most physicians, without necessarily articulating exactly what they are doing, apply a more crude, yet efficient approach. They preferentially prescribe therapies with established intrinsic efficacy and then use informal versions of “N-of-1” trials to fine-tune their choices over time.

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I agree that we make clinical decisions for the individual patients quickly and intuitively by taking the preferences or utilities of patients into account. The process is often iterative by trying different diagnoses or treatments / doses to optimise outcome, which is what I understand by ‘personalized’ medicine. This depends on reliable information that predicts outcomes with as much certainty as possible to minimise the number of iterations required.

This discussion was about a different matter. It was how to assess the ability of diagnostic information to allow diagnoses and decisions to be made as successfully as possible by leading to favourable outcomes as often as possible. If diagnostic tests and treatments are of little or no value, then we should not use them. The way diagnostic tests are assessed at present is very confused as illustrated by the bizarre example used by intelligent lay people and the discussion here that required a huge effort to disentangle. Poor assessment of diagnostic tests leads to a waste of money and clinicians’ time and needs sorting out. As you say, there is a danger that funding will be taken away from more rational approaches unless the latter can assert themselves.

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I’m completely sympathetic with your feeling here, Erin. My impression from the outset had been that the P&M argument was simply bad-faith shitposting, and that everybody responding to it on this thread was being taken for a ride. Your recent post in which P&M apparently double down on all this suggests now a different phenomenon is at work.

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Perhaps P&M did not set out in bad faith. Perhaps they were carried away by thinking that they had discovered something new and exciting to improve medical diagnosis and decision making by adding observational studies. They made many mistakes by trying to prove this using an implausible example and tortuous and erroneous reasoning. For example the RCT control group was identical in females and males so that P(y) – P(y_c) for females and males should be the same. However, for females they came up with an answer of 0.09 but instead of being the same for males it was 0.49. This is how they arrived at the ‘correct’ values p(Benefit) and P(Harm), - by making a mistake when calculating one of them. We would only be able to know which was wrong if they had not failed to specify the value of P(y) (which could have been 0.3 or 0.7 when the value of p(y_c) was 0.21).

The point is that they created the RCT and observational study results and the values of P(Benefit) and P(Harm) by allocating values to them. I have shown already that these values could have been estimated directly from the RCT using covariates without having to resort to an observational study. I have already raised this with P&M on twitter in the past but they do not appear to have understood. Erin pointed out that Judea Pearl continues to publicise their discovery in the preface to a new book this year, claiming to be the way forward. On the contrary, I think it is by a better use of covariates in diagnosis and treatment selection, especially by analysing their numerical values carefully prior to any dichotomisation.

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David

You might be right. If so, it’s important that criticism be properly contextualized. Questionable claims made in good faith by otherwise brilliant people, late in life, should not overshadow an impressive life’s work. I think that everyone in this forum would agree that compassion in such circumstances is essential. But it’s also important not to just sweep under the rug the harms that can flow when prominent figures dabble in fields they don’t understand. The COVID pandemic provided many disturbing examples of this phenomenon. People pay attention when prominent people speak, even if their ideas are (dangerously) off track.

I suspect that the author would have been more circumspect if he understood just how easily claims related to “personalized medicine” can be co-opted, in the modern world, by profit-seekers who lack any concern for patient welfare. These are not flames that most healthcare providers want to fan…

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Quite possible the post-hoc power team from MGH didn’t set out in bad faith, either; but we know how that ended up. The crucial principle is to keep the faith.

Indeed, and this the only reason I even addressed P&M in this thread. Their argument effectively “floods the zone” Steve Bannon style, “stinking up the joint” for people who are keeping the faith with the concepts of individuality and person-centeredness in medicine.

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You make another good point. There is more than one reason why people who actually work in the healthcare field might find claims like the ones in this paper so frustrating. Many physician scientists likely have a deep understanding of promising potential niches for more individualized approaches to therapy (e.g., dose selection to optimize risk/benefit; research to identify tumour markers that could result in development of new therapeutic targets…). But the expertise of these highly specialized applied scientists constrains them from making exuberant claims that might otherwise swallow huge sums of funding money and mislead upcoming young scientists.

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