Isn’t an RCT directly transportable to clinical settings? If an RCT shows ATE>0 , then, on average, the treatment has a positive effect. That seems like useful information in a clinical setting
The problem from a doctor’s point of view is that there varying degrees of disease severity, so that when the result of an RCT is applied to an individual patient the risk difference (AKA absolute risk reduction) has to take this into consideration. This means that baseline numerical test results that provide a measure of disease severity for the treatment and control groups have to be available from the RCT in order to make the above assessment.
By choosing treatment, confounding can occur.
A doctor advising a patient whether to accept or choose treatment will take into account not only the severity of the condition but also what dose to take. Both affect the intended effect of treatment but also its adverse effects. I can’t think offhand of an example where this process results in confounding, when the choice and ‘doing’ of treatment results in an increased frequency of a desired outcome over and above what would be expected from a RCT.
Why couldn’t this excess death rate be due to poor choices of treatment in the observational setting?
I suppose that this could happen if a drug was given inappropriately (e.g. giving intravenous fluids to someone in congestive heart failure already overloaded with fluid which would be perverse and malpractice). Excess death due to a drug would be by definition an adverse effect or ‘harm’. All treatment can cause benefit and harm but by different causal mechanisms. One cannot prove benefit or harm in an individual because as I understand it, many may experience a good outcome anyway without a drug (e.g. no MI without a statin) and many will also suffer what appears to be a drug’s adverse outcome without taking the drug (e.g. muscle pain without taking a statin).
Context is important so I would have thought any discussion about cause and effect must take place with detailed understanding of the biological mechanisms including the multiple feedback mechanisms involved. The result of such reasoning will always be a hypothesis that has to be tested by experimentation (i.e. RCTs, observational studies etc.), the results of which will be uncertain due to limited data and stochastic processes. I have experimented with fitting parametric distributions, splines and logistic regression functions and then placing confidence intervals on the estimated probabilities of outcomes conditional on disease severity, treatment and control and also confidence intervals on the differences between these probabilities.
Although I get the gist of the discussion between @Sander_Greenland, @F2harrell, @R_cubed, @Pavlos_Msaouel, @AndersHuitfeldt, @davidcnorrismd and @S_doi, I am still not clear as to how it relates in detail to my attempts to understand these processes. My aim is to foster a better mutual understanding between statisticians, researchers and practicing physicians. I would therefore be grateful to @Sander_Greenland especially and others if they could comment on the suggestions in my recent posts of Should one derive risk difference from the odds ratio? - #340 by HuwLlewelyn and Should one derive risk difference from the odds ratio? - #359 by HuwLlewelyn and how especially disease severity, treatment, control, probabilities of outcomes and their confidence limits relate to the expressions f(y_x;z,u) and E(y;x,z,u).