Why is the Bayesian method not employed for diagnosis in practice

Tom Benzoni benzonit at GMAIL.COM
Thu May 31 21:54:08 UTC 2018


What is often neglected in understanding the application of Bayes' theorem
is it establishes the PRE-test probability, not post-diagnosis.
Then the process self-learns, being modified by the output.

For example, you're screening for the presence/risk/chance of heart disease
by doing a cholesterol. (Don't belabor if this works.)
Upper limit of normal = 200; 2 people have 220 totals.

A comes from a family living into their 90s, taking an occasional vitamin.
B has a grandpa with first MI before 45 and a sibling with an MI @ 43.

Which person is more likely to have the disease in question?
Bayes helps answer this; it does not make the diagnosis.

In other words, the same test result returns a different disease
probability based on pre-existing factors.

This principle is obvious to practitioners but not so much to people who
feel testing is absolute.

Bayes' theorem can get a whole lot more wonky, but that's an application;
there are others.
I'll make other examples should that help.

Tom


On Thursday, May 31, 2018, Tibbits, Paul A. <Paul.Tibbits at va.gov> wrote:

> Tom,
> What wiki entry? Is there a link? Tnx. PT
>
>
>
> Sent with Good (www.good.com)
>
> ________________________________
> From: Tom Benzoni
> Sent: Wednesday, May 30, 2018 1:08:48 PM
> To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG
> Subject: [EXTERNAL] Re: [IMPROVEDX] Why is the Bayesian method not
> employed for diagnosis in practice
>
> I think there may be sone fundamental misunderstandings of Bayes' theorem
> and its application.
> The Wiki entry on the topic is quite good.
> Tom
>
> On Wednesday, May 30, 2018, Jain, Bimal P.,M.D. <BJAIN at partners.org
> <mailto:BJAIN at partners.org>> wrote:
> The reservation I have about the Bayesian method is about considering a
> prior probability which is a frequency in a population to be prior evidence
> for a disease in a given patient. This notion may make us not suspect a
> disease if its prior probability is low leading to a diagnostic error.
> It is of interest that in the two examples given by Dr. Elias, the
> presence of a disease is assessed in terms of a likelihood ratio, and not a
> prior probability. For example, IBS being more likely than splenic artery
> aneurysm given the GI symptoms means that the likelihood ratio of IBS is
> high compared to aneurysm.
> Similarly, the likelihood ratio of viral illness is high compared to strep
> throat given the nasal and respiratory symptoms.
> What these likelihood ratios are, I do not know, but they may be high
> enough, that further testing may not be required.
> Another example of a high likelihood ratio  for a disease is that for
> herpes zoster, given unilateral, blistering skin lesions in a dermatomal
> distribution which does not require further tests for a diagnosis.
> In diagnosis, in which our goal is accurate determination of a disease in
> a given patient, evidence from which we diagnose a disease is represented
> by a likelihood ratio as it represents a change in probability ( odds ) of
> a disease in a given patient , locating evidence in this particular patient.
> A probability, being a frequency in a population locates evidence in a
> population. Therefore, an inference from a probability  is made in a field
> such as life insurance business in which the goal is long run accuracy with
> tolerance for errors in some individual persons.
> I have never come across a diagnosis being made purely from a probability
> in the absence of data with significantly high likelihood ratio in
> published case discussions or in practice.
> There seems to be a general impression that evidence in an uncertain
> situation can only be represented by a probability and an inference made
> from it alone. This is not true, as there is flourishing school of
> inference from likelihood alone as seen in books by AWF Edwards, Richard
> Royall, Y. Pawitan and many more authors.
> A positive CT study for appendicitis has a likelihood ratio of 19, about
> the same for positive CT angiogram for pulmonary embolism. In practice,
> this study should allow us to diagnose appendicitis definitively with a
> high degree of accuracy in any patient, regardless of prior probability.
> It would be of interest to know the diagnostic accuracy of this test
> result across patients with varying prior probabilities  similar to the
> diagnostic accuracy of acute Q wave and ST elevation EKG changes for acute
> myocardial infarction being 85 percent in patients with varying prior
> probabilities.
>
> Bimal
>
> From: Elias Peter [mailto:pheski69 at GMAIL.COM<mailto:pheski69 at GMAIL.COM>]
> Sent: Sunday, May 27, 2018 12:12 PM
> To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:
> IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
> Subject: Re: [IMPROVEDX] Why is the Bayesian method not employed for
> diagnosis in practice
>
>
>         External Email - Use Caution
> I find the proposal that Bayesian analysis is not (or should not be) part
> of the initial diagnostic approach puzzling. The following examples
> illustrate why I feel that way:
>
>
>   *   The 30-year old who presents with 2 years of intermittent left upper
> quadrant pain and fluctuating bowel habits is more likely to have irritable
> bowel syndrome than splenic artery aneurysm. One would not start evaluation
> with imaging the LUQ.
>   *   The 8-year old with 4 days of runny nose, cough and sore throat is
> so much more likely to have a viral illness than a strep throat that a
> throat culture is poor medical practice.
>
> Peter Elias, MD
>
>
> On 2018.05.27, at 8:53 AM, Bruno, Michael <mbruno at PENNSTATEHEALTH.PSU.EDU
> <mailto:mbruno at PENNSTATEHEALTH.PSU.EDU>> wrote:
>
> Thanks Dr. Bimal for starting this interesting thread, and to Stefanie Lee
> for sharing that excellent 2006 paper from Blackmore, et al., as well as to
> Dr. Oldham for his insightful comments.
>
> I think the Blackmore paper is really touching on the topic of "signal
> detection theory," which is a very useful tool to understand about how
> useful information is actually extracted from complex data sets (like CT
> scans) that have extremely high levels of uncertainty built-in. We
> radiologists practice in a milieu of extraordinarily high undertainty, as I
> noted in my 2017 review in our Society's official journal, Diagnosis (DOI
> 10.1515/dx-2017-0006), so this is particularly relevant to us.
>
> I've attached an excellent PDF discussion of signal-detection theory as
> applied to diagnostic radiology to this message for anyone who might be
> interested.  While radiologists and others may recognize the use of ROC
> curves, which are a feature of this theory, they may not be as familiar
> with the concepts of d', a quantitative measure of how well the
> alternatives can actually be discriminated from the test, and the concept
> of "criterion," whereby the interpreter decides how sensitive vs. specific
> they wish to be.  This was the thrust of the Blakemore article, and his
> meta-analysis suggested that radiologists by-and-large got it right.  (For
> David Meyers--the attached PDF is in the public domain, provided by
> Professor David Heeger of NYU).
>
> Dr. Oldham also got it right, of course, saying that Baysean reasoning is
> not everything in diagnosis, but I believe that it takes us most of the
> way.  I very much appreciate Dr. Oldham's analogy that the radiologist is
> acting as the "expert witness" in a courtroom setting while the treating
> physician and patient / patient's family serves as the judge and jury, who
> are charged with ultimately deciding what "truth" is.
>
> Have a terrific Memorial Day Weekend, everybody!
>
> Mike
>
>
> ________________________________
> From: James Oldham <james.oldham at HEALTH.NSW.GOV.AU<mailto:
> james.oldham at HEALTH.NSW.GOV.AU>>
> Sent: Sunday, May 27, 2018 1:49 AM
> To: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:
> IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG>
> Subject: Re: [IMPROVEDX] Why is the Bayesian method not employed for
> diagnosis in practice
>
> Dear all
> There is a difference between the treating doctor who makes a
> probabilistic diagnosis and evidence providers such as radiologists who
> comment on phenomena the observe and interpret it - taking Bayesian prior
> probability calculations explicitly into their offerings. I think the
> testing physician and their patients and family as a collective Judge
> relying on evidence provided by expert witnesses. So this final stage will
> be informed by Bayesian logic but not ruled by it and certainly not trying
> to make a prediction- just best information to hand
> James Oldham
> Child Psychiatrist
> NSW
> From James Oldham - sent from my phone. (Please accept apologies odd
> sentence construction and creative additions courtesy of Apple autocorrect)
>
> On 27 May 2018, at 13:25, Stefanie Lee <stefanieylee at GMAIL.COM<mailto:
> stefanieylee at GMAIL.COM>> wrote:
> An interesting paper from Blackmore and Terasawa, JACR 2006 on the
> complexity of CT interpretation and how clinical probability may be
> factored into the decision to call a test result positive or negative:
> "In this paper, we present an example of how health utility assessment can
> be used to guide the optimum interpretation of an imaging test."
>
> "Radiologists have the ability to alter the sensitivity and specificity of
> their interpretations. For objective positivity criteria, different
> thresholds can be chosen to consider test results positive. For example,
> with appendicitis, one important factor in CT interpretation is the size of
> the appendix. Using a lower size threshold (eg, 6 mm) to consider an
> appendix abnormal will result in higher sensitivity for appendicitis, at
> the expense of lower specificity. Using a higher size measurement to
> consider the appendix abnormal (eg, 8 mm) would result in lower sensitivity
> but higher specificity. For subjective criteria such as periappendiceal fat
> stranding, the process is the same but less explicit. Individual
> radiologists can alter how much increase in the density of the fat is
> necessary to be considered abnormal “stranding.”"
> "In general, when a disease is rare and there are substantial costs to a
> false-positive diagnosis, interpreting a test with high specificity will
> maximize patient benefit. In contrast, when a disease is common and there
> are substantial costs for a false-negative diagnosis, interpretation at
> high sensitivity will maximize utility."
> "Our prior meta-analysis indicated that radiologists interpret CT scans
> with approximately equal sensitivity and specificity. The current analysis
> indicates that this is an appropriate threshold at the intermediate
> probability of disease at which CT is commonly used today. If CT is to be
> used in populations at higher or lower probabilities of disease, then
> different imaging thresholds will be appropriate."
>
> On 15 May 2018 at 13:18, Jain, Bimal P.,M.D. <BJAIN at partners.org<mailto:
> BJAIN at partners.org>> wrote:
> In this attached paper, I discuss that the prescribed Bayesian method is
> not employed for diagnosis in practice because probability of a diseases is
> considered evidence for it in a given patient in this method, which is
> incorrect as a probability is a frequency in a population. This leads to
> all sorts of errors in practice which I discuss.
> The correct method of diagnosis, which is employed in practice, consists
> of hypothesis generation and verification in which evidence is assessed by
> a likelihood ratio which locates it in the given patient of interest.
> Please review and comment on this paper.
>
> Thanks
>
> Bimal
>
> Bimal P Jain MD
> Northshore Medical Center
> Lynn MA 01904.
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> Moderator:David Meyers, Board Member, Society for Improving Diagnosis in
> Medicine
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> To learn more about SIDM visit:
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>
> ________________________________
>
> Address messages to: IMPROVEDX at LIST.IMPROVEDIAGNOSIS.ORG<mailto:
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> Moderator:David Meyers, Board Member, Society for Improving Diagnosis in
> Medicine
>
> To learn more about SIDM visit:
> http://www.improvediagnosis.org/
>
> ________________________________
>
>
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Moderator: David Meyers, Board Member, Society to Improve Diagnosis in Medicine


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