Whether physicians are aware of it or not, they use a Bayesian approach in their daily practice when they estimate the patient's probability of having condition X based on elements of the history and physical (i.e., pretest probability) before ordering any diagnostic tests. If available for condition X, a clinical prediction rule may be used. Although this process is very far from an exact science, it is often good enough to move the clinician's suspicion above the treatment threshold or below the diagnostic threshold (alternative diagnoses considered). Although most of us would like to see things fit a more more exact mathematical formula, it is rare (at least in emergency medicine) to be able to make very precise probability estimates at the individual patient-level.
Mike
Michael Brown, MD, MSc
Professor and Chair, Emergency Medicine
Michigan State University College of Human Medicine
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cell: 616-490-0920
On Feb 19, 2016, at 5:47 AM, Kevin Galbraith <[log in to unmask]> wrote:
> Hi there
>
> Can anyone advise: when calculating post-test probability of a diagnosis using the likelihood ratio for a diagnostic test, how do we make our best estimate of pre-test probability?
>
> I understand that prevalence is often taken as a pragmatic estimate of pre-test probability. But I assume a patient who presents with symptoms of the condition has, by definition, a pre-test probability that is greater than the prevalence in the wider (or preferably age/sex specific) population.
>
> To estimate pre-test probability, are we reliant on finding an estimate from an epidemiological study whose subjects most closely reflect the characteristics of our individual patient? This would seem a serious limitation to the utility of the Bayesian approach.
>
> Thanks
>
> Kevin Galbraith
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