Dichotomization

It’s crucial that the historical roots of the responder analysis fiasco be diagnosed accurately- otherwise, proposed treatments to abolish it won’t work. To this end, it feels like its absolute crux is a failure to understand the concept of “causal nonidentifiability,” as explained above by Dr.Greenland.

The question that drug regulators were asking in the 1990s was “what proportion of patients will benefit from this treatment that we are being asked to approve”? Instead of drug sponsors telling regulators that they were asking an unanswerable question, they dutifully tried to provide the answer. But the technique they proposed to answer the question was causally unsound.

The next question to ask is: why didn’t statisticians employed by drug companies at the time rise up en masse against this unreasonable request from regulators? And why didn’t regulators understand causal nonidentifiability well enough that they knew not to make the request in the first place ?? Is it because the concept simply wasn’t widely understood at the time among statisticians? This seems like a reasonable conclusion, given that a widely-cited 2016 publication by a statistician further reinforced the error and the practice of responder analysis remains alive and well today.

It seems to me that without this misunderstanding around causal nonidentifiability, responder analysis never would have caught on in the first place. Therefore, isn’t wide propagation of a plain language explanation of causal nonidentifiability what will be needed to abolish it? If responder analysis is the Death Star, then causal nonidentifiability seems like the reactor core- target that issue and the whole pernicious, fortified sphere of misunderstanding will blow up.

@Pavlos_Msaouel is pulling his hair out over the fact that randomized non-comparative trials (RNCTs) seem to be catching on in oncology. He is witnessing the birth of another Frankensteinian statistical practice. Statisticians are again chucking statistical fundamentals out the window in trying to address an intractable clinical question: how to make reliable inferences from small numbers of patients?