Why is the Bayesian method not employed for diagnosis in practice

Stefanie Lee stefanieylee at GMAIL.COM
Sat May 26 22:05:06 UTC 2018


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> 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 to Improve Diagnosis in Medicine


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