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A History of Evidence in Medical Decisions: from the Diagnostic Sign to Bayesian Inference

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A History of Evidence in Medical Decisions: from the Diagnostic Sign to Bayesian Inference BRIEF REPORTS A History of Evidence in Medical Decisions: From the Diagnostic Sign to Bayesian Inference Dennis J. Mazur, MD, PhD Bayesian inference in medical decision making is a concept through the development of probability theory based on con- that has a long history with 3 essential developments: 1) the siderations of games of chance, and ending with the work of recognition of the need for data (demonstrable scientific Jakob Bernoulli, Laplace, and others, we will examine how evidence), 2) the development of probability, and 3) the Bayesian inference developed. Key words: evidence; games development of inverse probability. Beginning with the of chance; history of evidence; inference; probability. (Med demonstrative evidence of the physician’s sign, continuing Decis Making 2012;32:227–231) here are many senses of the term evidence in the Hacking argues that when humans began to examine Thistory of medicine other than scientifically things pointing beyond themselves to other things, gathered (collected) evidence. Historically, evidence we are examining a new type of evidence to support was first based on testimony and opinion by those the truth or falsehood of a scientific proposition respected for medical expertise. independent of testimony or opinion. This form of evidence can also be used to challenge the data themselves in supporting the truth or falsehood of SIGN AS EVIDENCE IN MEDICINE a proposition. In this treatment of the evidence contained in the Ian Hacking singles out the physician’s sign as the 1 physician’s diagnostic sign, Hacking is examining first example of demonstrable scientific evidence. evidence as a phenomenon of the ‘‘low sciences’’ Once the physician’s sign was recognized as of the Renaissance, which included alchemy, astrol- a form of evidence, numbers could be attached to ogy, geology, and medicine.1 The low sciences were these signs. Signs recorded by physicians in the contrasted with the ‘‘high sciences’’ of the Renais- same patient (or set of patients) over time supported sance, namely optics, astronomy, and mechanics, a claim by a physician that what is going on in which are characterized by signs with measurable a patient at a certain time is related to what is going precisions. Whereas the low sciences treated evi- on in that patient at a later time. In contrast to testi- dence in terms of opinion and testimony, the high mony and opinion, this new form of evidence is sciences according to Hacking could scorn opinion described by Hacking as that form of evidence where 1, pp 36-37 as a form of evidence and instead begin to rely on one thing points to another. Hacking gives demonstration. Hacking argues that only ‘‘diagno- an example of a thing (a precipitate) as evidence 1 sis’’ places the physician on a different plane of evi- for another thing (the state of a person’s insides). dence than that of other practitioners of the low sciences. Received 06 May 2011 from Department of Veterans Administration Medical Center, Oregon Health and Science University, Portland, OR FORWARD PROBABILITY IN THE HIGH (DJM). Revision accepted for publication 03 November 2011. SCIENCES Address correspondence to Dennis J. Mazur, Medical Service (P3-MED), Oregon Health and Science University, 3710 S.W. U.S. Vet- The physician’s diagnostic sign, confirmed over erans Hospital Road, Portland, OR 97201; telephone: (503) 220-8262; time and enumerated in terms of successful v. e-mail: [email protected]. unsuccessful observations, provides a logic of num- DOI: 10.1177/0272989X11434739 bers. However, a logic of numbers alone could not be MEDICAL DECISION MAKING/MAR–APR 2012 227 Downloaded from mdm.sagepub.com at MCGILL UNIVERSITY LIBRARY on November 19, 2012 MAZUR used to explain the phenomena of the high sciences, the letters and works of Pascal, Fermat, and Huy- that is, explain the phenomenon of optics, astron- gens.6 Their focus instead was on the ‘‘the number omy, or mechanics. Instead, the high sciences needed between zero and one’’ that was the proportion of a new form of mathematics more common to the gam- the stakes due a player in a game of chance. Pascal, bler than to the scientist, that is, a logic of probability. Fermat, and Huygens were interested in predicting Hacking argues that this logic of probability was from a set of past observations of fair events (what based on games of chance such as the flips of a fair Huygens referred to as fair lays) what will happen coin recorded over time and on an individual’s will- in the future. However, if the events being consid- ingness to pay a sum to play such a game of chance. ered are not countable and are not fair events, then Bernstein notes that the Chevalier de Me´re´ was one can no longer base decision making on their known to bet repeatedly on games of chance with approach to the computation of chances. If this is just a narrow margin in his favor.2 Bernstein alludes forward probability, one may ask what is backward to Pascal’s recognition that de Me´re´ knew that the (inverse, reverse) probability and where do the ori- probability of throwing a 6 with one die rises above gins of that concept lie? 50% with 4 throws to 51.77469136% and that de Me´re´ was a successful gambler winning small amounts over a large number of throws of a die.2 BACKWARD PROBABILITY However, at one point the Chevalier changed his game and bet he would roll a total of 12, or a double Backward (inverse, reverse) probability has a his- 6 (also known as the sonnez3) on 24 rolls of 2 dice. tory in the 1800s that involves 3 key figures: Jakob Soon he realized that the new game was a losing Bernoulli, Thomas Bayes, and Pierre Simon Laplace. proposition, and Leibniz notes that the Chevalier It is a time when the issues of original ideas (who asked Blaise Pascal why his new game was not as was first to develop a concept, who was first to profitable as his original. Pascal, intrigued by de apply the concept, and attribution of ideas to origi- Me´re´’s question, passed the question to Pierre de nators) are less than straightforward. Fermat, resulting in an exchange of letters. Pascal worked through the problem and found that the Jakob Bernoulli (1654–1705) probability of winning the new game was only 49.1% compared with 51.8% for the old game. Sha- Shafer credits Jakob Bernoulli’s theorem and his fer credits Pascal and Fermat with founding mathe- argument that it is morally certain that the frequency matical probability based on their work on fair of an event in a large number of trials will approxi- odds in games of chance and solving the problem mate its probability as a notion closer to what we con- of points (the problem of equitably dividing the sider today as probability. For Bernoulli, ‘‘the more stakes when a fair game is halted before either player observations that are taken, the less the danger will has enough points to win).4 Shafer considers Chris- be of deviating from the truth.’’7(p257) (See Hald’s dis- tiaan Huygens’s De Ratiociniis in Aleae Ludo (On cussion of Jakob Bernoulli’s theorem.7) Shafer sees the Calculations in Games of Chance) in 1657, pos- the work of Bernoulli and De Moivre as a precursor sibly the first written text on probability, with popu- to the modern theory of confidence intervals.6 larizing the main ideas of Pascal and Fermat. Jakob Bernoulli included Huygen’s book on the But probability derivations based on considera- computation of chances in fair lays within his tions of games of chance using fair flips of fair coins book, Ars Conjectandi (1713).8 Within Ars Conjec- alone could not found the basis of the use of data in tandi, Jakob Bernoulli makes points that can be the sciences based on prior evidence. Games of interpreted as an early position that the computation chance could yield insights on expected utility5 of chances can be ‘‘inverted’’ and then ‘‘suggests’’ but could not be used to infer odds of future occur- that this process can be applied to make a guess. rences based on past probabilities where there were For Bernoulli, this guess would be about the proba- no assumptions about countable outcomes—known bility of an event occurring given a set of observa- and specifiable before an event occurs. For example, tions about previous outcomes that need not be in dice and cards, all countable outcomes are speci- considered as necessarily countable or fair events. fiable before any game with fair pieces (dice or Dale notes that in Ars Conjectandi there may be an cards) is played fairly. intent to apply probability theory in an inverse man- Shafer notes that the term probability was not ner. However, Dale can find no application of such used in discussions related to games of chance in a result in Ars Conjectandi.9 Stigler10, p.10 argues 228 MEDICAL DECISION MAKING/MAR–APR 2012 Downloaded from mdm.sagepub.com at MCGILL UNIVERSITY LIBRARY on November 19, 2012 DIAGNOSTIC SIGN TO BAYESIAN INFERENCE that the ‘‘chief conceptual step’’ taken in the 18th universities because patrons were charged an century toward the application of probability to entrance fee of 1 penny to hear the lecture. In proba- quantitative inference involved ‘‘the inversion of bility, Simpson is known for his work on proving that the probabilities’’ analyses of Jacob Bernoulli and the arithmetic mean of a number of observations is of de Moivre. Stigler also argues that what was needed more analytical value than a single one.14 De Moivre at this time was a step away from consideration of first published The Doctrine of Chance: A Method of games of chance involving coins and dice where Calculating the Probabilities of Events in Play in the benefits of inverse inference were not obvious.
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