This comment touches on the central tension between the current paradigm in medicine, i.e. “evidence-based medicine” and an alternative and intuitively appealing approach based on a biological understanding of mechanism of disease.
In evidence-based medicine, decisions are based on statistical analysis of randomized trials; what matters is whether we can be confident that the medication probabilistically has improved outcomes when tested on humans as a unit. We don’t care really care too much about the mechanism behind the causal effect, just whether we can be sure it is real.
The exaggerated strawman alternative approach to EBM would be Star Trek medicine, where the ship’s doctor can reliably scan an alien’s biology, determine which molecule is needed to correct the pathology, synthesize that molecule and administer it as treatment.
If we have a complete understanding of what Nancy Cartwright calls “the nomological machine”, Star Trek medicine should work in theory. However, you are going to need a very complete, accurate and detailed map of the human body to make it work. Given the complexity of the human body, I think we are very far from being able to do this in practice.
There have been many cases in recent history where doctors believed they understood biology well enough to predict the consequences, yet were proved wrong by randomized trials. See for example Vinay Prasad’s book “Ending Medical Reversal”.
My personal view is that we are very far from being able to ground clinical decisions in mechanistic knowledge instead of randomized trials. Trying to do so would probably be dangerous given the current state of biological understanding. However, we can probably improve on naive evidence-based medicine by carving out a role for mechanistic knowledge to complement data analysis. Mechanisms seems particularly important for reasoning correctly about extrapolation, the purpose of my research program is to clarify one way such mechanisms can be used. It doesn’t always work perfectly, but I am not aware of any examples where an alternative approach works better.
This comment touches on the central tension between the current paradigm in medicine, i.e. “evidence-based medicine” and an alternative and intuitively appealing approach based on a biological understanding of mechanism of disease.
In evidence-based medicine, decisions are based on statistical analysis of randomized trials; what matters is whether we can be confident that the medication probabilistically has improved outcomes when tested on humans as a unit. We don’t care really care too much about the mechanism behind the causal effect, just whether we can be sure it is real.
The exaggerated strawman alternative approach to EBM would be Star Trek medicine, where the ship’s doctor can reliably scan an alien’s biology, determine which molecule is needed to correct the pathology, synthesize that molecule and administer it as treatment.
If we have a complete understanding of what Nancy Cartwright calls “the nomological machine”, Star Trek medicine should work in theory. However, you are going to need a very complete, accurate and detailed map of the human body to make it work. Given the complexity of the human body, I think we are very far from being able to do this in practice.
There have been many cases in recent history where doctors believed they understood biology well enough to predict the consequences, yet were proved wrong by randomized trials. See for example Vinay Prasad’s book “Ending Medical Reversal”.
My personal view is that we are very far from being able to ground clinical decisions in mechanistic knowledge instead of randomized trials. Trying to do so would probably be dangerous given the current state of biological understanding. However, we can probably improve on naive evidence-based medicine by carving out a role for mechanistic knowledge to complement data analysis. Mechanisms seems particularly important for reasoning correctly about extrapolation, the purpose of my research program is to clarify one way such mechanisms can be used. It doesn’t always work perfectly, but I am not aware of any examples where an alternative approach works better.