In the previous article, the logical representation of medical science claims was investigated. Scientific claims have the goal of compressing experimental evidence into rules or models that reliably represent the evidence and make good predictions about future events. In this article, a step is taken back and the representation of individual medical events is investigated. For example, the individual events that together make up the evidence used to come to a medical science claim.
Individual medical events are simpler to model and appear to be well modeled by the standard Davidsonian analysis discussed in the previous article (for discussion of why this analysis is used refer to the previous article). Below is an example discussed in the previous article:
(1) a. Statins reduce myocardial infarction in normal humans
This Davidsonian analysis of the statement (16a) was taken as a step in the investigation (provided above as (1)), but it was decided that that the existential quantifier was not the correct analysis of the statement and that scientific claims like that in (1a) should to be interpreted as universal quantification. If an individual participant in the clinical trial is considered, the following statement could be made:
(1) a. Statins reduced myocardial infarction in John Smith
Here an important difference can be seen between the individual cases and the claims that can be made via statistical analysis of a large number of individual cases as a group. It may be that the statins did reduce the likelihood of a myocardial infarction in John Smith. However, given that the actual event of a myocardial infarction is an irregular occurrence and is dependent on a large number of factors (eg. lifestyle, high physical/stress events, etc), and these factors are uncertain, there is no way of being confident that the statins had any effect from the evidence of one individual. This is why clinical trials are required. So that outside factors can be controlled to some degree and the number of participants can be such that statistically we can have reasonable confidence that the medical scientific claim is valid.
In the example above, the frequency of myocardial infarction is low in an individual and the result often catastrophic. For this reason, studies of drugs for diseases like heart disease generally need to be long term and inclusive of end points (eg. death). If a disease that involved continuous or frequent disease events is considered, then a sentence like that in (1a) may make sense in that the frequency of disease events prior to introduction of the drug can be compared to the frequency afterwards. Take for example antibiotic treatment for bacterial infection:
(2) a. Antibiotics reduce bacterial infection in John Smith
In this example, bacterial infection nay occurred in John Smith a large number of times during his life. To assess the validity of the claim in (2) there would need to be a number of bacterial infections where no antibiotics were administered and then a number of bacterial infections where antibiotics were administered to allow for a comparison of the control events (no antibiotics) to treatment events. Where the difference was significant, the treatment might be considered a success and the claim (2) might be considered as true (uncertainty discussed further below). However, here again we see the importance of a clinical trial to assess the efficacy of a drug. The claim in (2) is specific to “in John Smith” and the claim cannot be extended to “normal humans” generally. While John Smith is a human, he also has a unique genome, diet, lifestyle and life history generally. The effectiveness of a drug in one human is certainly support for its effectiveness in other humans, but it is always possible that the drug only works with John Smith’s specific genetic profile for example. There is also the possibility that the bacterial infection went away by chance at the same time John Smith began receiving the antibiotic or that it resolved by the placebo effect. This last point highlights the fact that the reduction does not actually imply causation.
In the case of heart disease, the primary cause of myocardial infarction, there may be an indicator of disease other than myocardial infarction. Atherosclerosis describes the formation of lesions in the arteries. Atherosclerosis in the coronary arteries (arteries that supply blood to the heart) is a primary indicator of heart disease and increases the chance of myocardial infarction. The atherosclerotic lesions in the arteries (lesions from here on) exist prior to diagnosis of heart disease and after the commencement of treatment. So in theory they could be measured at many time points prior to treatment and after treatment with statins. If the lesions regressed after treatment, then this could be an indicator within one individual that statins reduce heart disease and by extension myocardial infarction. Unfortunately, lesion progression prior to diagnosis is rarely measured outside of specialised scientific studies. So there is no significant series of events prior to treatment. Secondly, lesions rarely regress significantly and the goal is usually to slow progression of lesions. To assess reduced progression would require the collection of far more time series events that included measures of magnitude and not just the presence or absence of lesions. Unlike say a skin infection, where the infection is visible externally with accompanying pain, the progression of atherosclerotic lesions is painless and difficult to assess given their location with our current technology. Finally, the evidence that lesion stage or size is positively correlated with myocardial infarction events is not definitive. For heart disease and many other diseases, the assessment of the performance of a drug is not possible within an individual, either due to the lack of time series data, the lack of a reliable indicator of disease or the requirement to base the assessment on the reduction of death in a cohort.
We have discussed the need for group based research, such as clinical trials, as necessary for making statements about the correlation between drugs and diseases. Well-designed clinical trials should be able to substantially exclude the placebo effect and take into account the performance of drugs based on end points like death, but it should be noted that chance can still never be ruled out as a factor. In studies involving chemicals or cells the sample size can be made very large, but with humans it is difficult to get very large sample sizes and to control all important variables. However, the statin meta analyses included over 90,000 participants through the consolidation of many large studies.
Version 1.0, 30th September, 2016