I very recently read where the Merck V920 Ebola Zaire vaccine received a positive opinion form the Committee for Medicinal Products for Human Use (CHMP) of the European Medicines Agency (EMA), which recommended the vaccine for conditional marketing authorization . This reminded me of an article in the January 4th, 2019 issue of STAT Health where I came across an article on the Ebola outbreak that is currently ongoing in the Democratic Republic of Congo . Besides battling the unseen virus, health care workers from WHO and other governmental and non-governmental agencies are risking their lives in regions where rebels are fighting Congolese forces. This was and is complicating medical relief efforts and very importantly the testing of the experimental V920 Ebola vaccine from Merck.
What caught my attention were some of the statistics and claims that were made in the STAT Health article. Two quotes from the article are given below.
- “The Ebola outbreak in the Democratic Republic of the Congo is the second worst on record, having topped 600 cases. But the case count would be much higher still if an experimental Ebola vaccine were not being used to contain spread of the disease, the director-general of the World Health Organization said Thursday.” [This was attributed to Tedros Adhanom Ghebreyesus, the director-general of WHO.]
- “The assessment of vaccine’s effectiveness by Tedros, as he is known, is based on the fact that the case count hasn’t grown exponentially, not on modeling calculations.”
The latter statement made me chuckle: “not on modeling calculations.” Yet in fact, it is based on their observation that the number of cases is NOT following an exponential model. Clearly, it is based on modeling calculations. The following diagram is strictly illustrative of the point (I do not have access to the data). One cannot claim that lives have been saved without some sort of underlying model of expected deaths in the absence of the vaccine.
While this is what immediately struck me, it got me to thinking about the notion of implicit and explicit models. Most everything I have read on the topic of human cognition declares that our minds (consciously or subconsciously) are always working to categorize events/people and to put them into the context of our lives and experiences. Perhaps our minds tell us (consciously or subconsciously) “The last time I ran into someone dressed like that or behaving like that, they were not very friendly or they were not very open to my ideas.” Then we may act more cautiously or more boldly when we share an idea in a meeting. Our minds are constantly building models of the world so that we can make decisions on what to expect and how to act in that world.
I will use this occasion to make this point in the context of frequentist versus Bayesian statistics. As many of my readers may know, I am not educated as a Bayesian, but rather a more recent convert to this way of thinking (as I learned more about it). As I have espoused any Bayesian analysis or approach, the most persistent resistant questions are, “Where does the prior come from?” or “Isn’t the prior subjective?” Of course this is true, BUT WHO CARES! Upon completion of a frequentist analysis and its resulting p-value, the interpretation of the result is usually put into the context of prior knowledge or belief.
For example, if a researcher from a relatively unknown lab produces a novel experimental drug derived from a common dandelion weed, then does a clinical study in pancreatic cancer and declares that the new drug is effective because he/she obtained a p-value of 0.04 for extending overall survival, would not the whole world be skeptical of the finding? Why? Because we know that pancreatic cancer is very difficult to treat. Because we know that many treatments have failed and even the best treatments (approved by regulators through rigorous clinical development programs) based on the best molecular biological knowledge provide only modest gains in survival. Thus, it is difficult to put any p-value from a single experiment into context without some underlying (mathematical or mental) model of what to expect.
Such models are often implicit (subconscious) and based on the individual scientist’s knowledge and experience. One scientist knows the researcher and has more trust in the results. Another researcher doesn’t believe that anything useful could come from a dandelion plant and disregards the findings. Should the findings be pursued with more research? Maybe or maybe not. It depends on whether one thinks it is throwing good money after bad or whether any potentially positive finding for a difficult and terminal disease is worthy of additional research.
The argument for a Bayesian approach is that at least it attempts to quantify the prior knowledge in explicit mathematical expressions (conscious). While there is subjectivity to this endeavor, at least the assumptions and expectations are transparent. As such, this is one reason (there are others that have been noted in other blogs) I tend to favor the Bayesian paradigm. My consulting experience for the last 38 years in pharma related research has taught me that surfacing implicit knowledge and understanding brings greater clarity to any scientific discussion. Phrases like, “There is a reasonable chance of XYZ …” are refined to “There is a 35% chance of XYZ …” or better yet, “The distribution for the measurement of XYZ is …”
I end with my favorite quote about models (no, it’s not George Box!). It comes from a most unusual place … a geo-political book entitled The Clash of Civilizations and the Remaking of World Order by Samuel P. Huntington. I hope you enjoy this lucid description of cognition, cartography and modeling. Huntington wrote this:
“If we are to think seriously about the world, and act effectively in it, some sort of simplified map of reality, some theory, concept, model, paradigm, is necessary. Without such intellectual constructs, there is, as William James said, only ‘a bloomin’ buzzin’ confusion.’ Intellectual and scientific advance, Thomas Kuhn showed in his classic [book] The Structure of Scientific Revolutions, consists of the displacement of one paradigm, which has become increasingly incapable of explaining new or newly discovered facts, by a new paradigm, which does account for those facts in a more satisfactory fashion. ‘To be accepted as a paradigm,’ Kuhn wrote, ‘a theory must seem better than its competitors, but it need not, and in fact never does, explain all the facts with which it can be confronted.’ ‘Finding one’s way through unfamiliar terrain,’ John Lewis Gaddis also wisely observed, ‘generally requires a map of some sort. Cartography, like cognition itself, is a necessary simplification that allows us to see where we are, and where we may be going.‘ [My emphasis added since it is my favorite sentence in the whole book.]
“Simplified paradigms or maps are indispensable for human thought and action. On the one hand, we may explicitly formulate theories or models and consciously use them to guide our behavior. Alternatively, we may deny the need for such guides and assume that we will act only in terms of specific ‘objective’ facts, dealing with each case ‘on its merits.’ If we assume this, however, we delude ourselves. For in the back of our minds are hidden assumptions, biases, and prejudices that determine how we perceive reality, what facts we look at, and how we judge their importance and merits. We need explicit or implicit models … to be able to:
1. order and generalize about reality;
2. understand causal relationships;
3. anticipate and, if we are lucky, predict future developments;
4. distinguish what is important from what is unimportant; and
5. show us what paths we should take to achieve our goals.
“Every model or map is an abstraction and will be more useful for some purposes than for others. A road map shows us how to drive from A to B, but will not be very useful if we are piloting a plane, in which case we will want a map highlighting airfields, radio beacons, flight paths, and topography. With no map, however, we will be lost. The more detailed a map is the more fully it will reflect reality. An extremely detailed map, however, will not be useful for many purposes. If we wish to get from one big city to another on a major expressway, we do not need and may find confusing a map which includes much information unrelated to automotive transportation and in which the major highways are lost in a complex mass of secondary roads. A map, on the other hand, which had only one expressway on it would eliminate much reality and limit our ability to find alternative routes if the expressway were blocked by a major accident. In short, we need a map that both portrays reality and simplifies reality in a way that best serves our purposes.”
Now that is some really good analytical thinking!
 Merck Receives EU CHMP Positive Opinion for Investigational V920 Ebola Zaire Vaccine for Protection Against Ebola Virus Disease. https://www.mrknewsroom.com/news-release/ebola/merck-receives-eu-chmp-positive-opinion-investigational-v920-ebola-zaire-vaccine- (accessed 23 Oct 19).
 STAT Health (4 Jan 2019). WHO’s Tedros: Experimental Ebola vaccine in the DRC has saved countless lives. https://www.statnews.com/2019/01/04/ebola-vaccine-tedros-drc/?utm_source=STAT+Newsletters&utm_campaign=8cc59af83f-MR_COPY_08&utm_medium=email&utm_term=0_8cab1d7961-8cc59af83f-150963065