I agree. In clinical practice I have found that relative measures such as HRs (and their compatibility intervals) are what I mainly need from RCTs. The second important piece I need for patient-centric decision-making is an idea of the patient’s prognosis, e.g., using risk scores usually derived from observational data, although they can be derived from large RCTs as well. The third piece is to know what are the patient’s wishes.
The reason why “absolute benefit” from an RCT is in itself actually relative is because there are different ways to define absolute benefit and, based on which “absolute benefit” we focus on, we can make opposite conclusions and decisions as described very well here. Here is an example: imagine an RCT in oncology with primary outcome of overall survival (OS) that produces a HR = 0.5 favoring the new treatment, and where for the sake of simplicity all typical assumptions hold: proportional hazards with constant event rate over time (exponential distribution of OS). If the population accrued has good prognosis with median OS for the control group of 18 months then the median OS for the new treatment would be 36 months. That is an “absolute benefit” of 18 months which sounds pretty good for the new treatment. But in terms of hazard rates the control group had a hazard rate of Ln(2)/18 = 0.039 whereas the treatment group had a hazard rate of Ln(2)/36=0.019. Which is not that big of a difference in absolute hazards. This makes sense because at any point in time these patients have a low instantaneous hazard of dying. Giving them the new treatment (which may for example be more toxic or more expensive or more logistically demanding) will not change that by much. A patient with that prognosis who for example wants to live long enough to see her daughter graduate college in 3 months may decide that she will not benefit much by the new treatment over the control. Indeed, the 3-month survival probability increases with the new treatment to just 94% compared with 89%, i.e., a 5% increase.
Now if the population accrued has poor prognosis then the decision-making can drastically change. The HR is still 0.5 but if, say, the median OS in the control group is 2 months then the median OS in the new treatment group is 4 months. A mere 2 month-benefit in OS. However, in terms of hazard rates, the patient on the new treatment gets her hazard cut down from 0.35 to 0.17, which is a much higher absolute difference in hazards than in the previous scenario. For a patient with this prognosis who wants to live 3 months to see her daughter graduate, the new treatment should be strongly considered, even if it has unfavorable side-effects or is logistically more challenging. This is because now, the 3-month survival probability increases with the new treatment to 60% compared with 35%.
Notice that all that I needed in the above exercise was the patient’s specific goals, the HR = 0.5 from the RCT, and the patient’s prognosis (not necessarily derived from RCT data). The “minimum
clinically significant AB” in the ESMO-MCBS tool indirectly reflects prognosis for the RCT patients and what the clinicians think would be meaningful for the patient. But I personally prefer to directly use prognosis and ask the patients themselves what they find more meaningful.