Using probabilistic reasoning
BMJ 2009; 339 doi: https://doi.org/10.1136/bmj.b3823 (Published 03 November 2009) Cite this as: BMJ 2009;339:b3823
- Jenny Doust, professor of public health
- 1Faculty of Health Sciences and Medicine, Bond University, Gold Coast, QLD 4029, Australia
- jdoust{at}bond.edu.au
Diagnostic tests—whether clinical signs, imaging, or laboratory tests—are imperfect: there is always a possibility that test results are inaccurate and our diagnosis is wrong. However, we need to make decisions about whether to treat or not to treat patients, and so we need to feel confident that our diagnosis is above a certain threshold before we decide to treat a patient and below a certain threshold if we decide to withhold treatment. The threshold depends on the disease and the potential harms and benefits of treating or not treating patients. Unless we have clear strategies to cope with the uncertainties of testing, false positive results mislead us to treat some patients unnecessarily and false negative results lead us to fail to treat some patients adequately or in time.
What is probabilistic reasoning?
Probabilistic reasoning is used when we consider the diagnostic accuracy of tests in our clinical decisions. It is also called Bayesian reasoning, being based on Bayes’ theorem, in which the probability of a hypothesis is modified by further data.. As primary care doctors, we use tests every day to decide whether our patients have a particular disease, but we often ignore the uncertainty inherent in the test results. Only rarely can we define how well a test rules in or rules out a disease. Does this matter?
An example of probability revision
We can combine how likely it is that a patient has a disease before having the test (the pretest probability) with the accuracy of the diagnostic test (the sensitivity and specificity) to calculate the probability that a patient has a disease after having the test (the post-test probability). As an example, …
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