An 18th Century Clergyman's Contribution to 21st Century Medicine
DrBudWiederman, MD, MA, Evidence eMended Editor, Grand Rounds
I'm using my "5th Tuesday" commentary option this month to mention not an original
study, but rather an accompanying editorial for an article on hypothermia treatment
for neonatal hypoxic ischemic encephalopathy. I hope the original article itself appears
in an upcoming AAP Grand Rounds series, maybe I'll include it in a subsequent commentary
if it does.
Quintana M, Lewis RJ. Bayesian analysis: using prior information to interpret the
results of clinical trials. JAMA 2017; 318:1605-6. doi:10.1001/jama.2017.15574.
Thomas Bayes, in addition to being a Presbyterian minister, also was an accomplished
statistician. I suspect he never expected how his eponymous Theorem would impact the
The editorial article cited above is part of a series in this journal, the JAMA Guide
to Statistics and Methods. Unfortunately, I haven't found a foolproof method to search
this collection separately, so I can't give you a decent link to it. I'd suggest googling.
The editorial primarily discusses how Bayesian statistical analyses are helpful in
clinical situations where large quantities of patient data are not available, allowing
for synthesis of data from multiple sources. Here, though, I'd like to focus on the
importance of Bayesian reasoning in clinical medicine. I consider it an important
road forward in evidence-based clinical practice.
For starters, let me tell you about 2 examples from my prior EBM teaching to highlight
the importance of pretest probability in clinical decision making.
First, take yourself out of your pediatric mindset and imagine a 64 year old gentleman
seeking your care for a 3-week history of early morning vomiting. You decide to order
a urine pregnancy test, and the result is positive. What is the probability that this
patient is pregnant? I hope you'll come to the conclusion that the answer is zero,
given both sex and age of the patient, and that you shouldn't have ordered the test
in the first place. This is a situation where the pretest probability is zero, so the test result doesn't matter.
Another example I've used is ordering a stool rotavirus assay in a 4 month old child
with acute diarrhea. If this child lives in a more northern climate, where rotavirus
is highly seasonal and occurs almost exclusively in the winter months, a positive
result in the summer approximates the male pregnancy absurdity above (unless the child
has recently traveled to the southern hemisphere). Furthermore, even if you ordered
it in the winter, in our era of rotavirus immunization a positive result might more
likely reflect the fact that the infant has been immunized, rather than being the
diagnostic answer. Again, pretest probabilities greatly impact how we (should) use
To add a bit more numbers to the question, here's an example widely studied and used:
One percent of 40 year old women who have routine mammography have breast cancer.
Eighty percent of women with breast cancer have positive mammograms. Women without
breast cancer have positive mammograms 9.6% of the time. A 40 year old woman seeking
your care has a positive mammogram in a routine screening. What is the probability
that she actually has breast cancer?
Take a couple minutes to think about your answer. Note that studies have shown only
15% of physicians get the correct answer. You can find a good discussion of this problem
online. It will show you how Bayesian reasoning, whereby the clinician considers pretest
probabilities prior to deciding on tests or treatments, can improve outcomes.
If you want a great explanation of Bayesian reasoning, with a choice of how you want
to view the explanations, try out this new site; I recommend it highly.
If we all used Bayesian reasoning in our everyday clinical practice, we'd like save
a lot of money, and our patients would have better outcomes. Think about it.
Oh, and the correct answer to the screening mammography problem is that 7.8% of the
women with positive screening mammographies will actually have breast cancer.