One theory is that physicians gave too much credence to hypothesised mechanisms for which there was little or no evidence. If so, the problem was one of method: too little time spent obtaining solid evidence of mechanisms and too little inclination to properly evaluate the evidence that was available.
I’d originally planned to write something this week on the announcement that the Nobel prize in Physiology/Medicine has been awarded to Campbell, Ōmura and Tu. While there’s lots of possible interest here – the Neglected Tropical Disease angle, or the unusual military aspect to be found in the intellectual history of Tu’s work on artemisinin. However, I’ve been distracted by something that came out of S. Lochlann Jain’s excellent new-ish book Malignant: How Cancer Becomes Us, which I’ve been avidly reading this week.
We all know that correlation is not causation. A correlation can be due to factors other than causation, such as bias, confounding, chance, time-series trends (e.g., the correlation between British bread prices and the sea level in Venice), or semantic, logical, physical or mathematical connections. In order to rule out these alternative explanations of a correlation – and establish causation – we need to seek evidence of a mechanism. One needs to account for the correlation via some mechanism by which the putative cause brings about the putative effect.
Suppose a patient P is informed by a medical doctor that she has a certain disease and that she is expected to survive for two further years. What consequences for her life should P draw from this information? It seems that without further knowledge, she can draw no precise consequences at all. P has to know how the doctor came to this decision. This is so because of the reference-class problem.
In an earlier post I suggested that systems medicine, a new approach to medicine which applies the ‘big data’ approach of bioinformatics, offers substantial promise, but also faces profound challenges, not least the question as to how integrate multifarious sources of evidence in order to discover new causal relationships.