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Tools for Decision Making Sections
Author Bio
Introduction
Currently selected section: Probability Theory
Case Study 1: Patient History
Bayes' Theorem
Methods for Estimating Pre-test Probability
Estimating Likelihood Ratios
Sensitivity and Specificity
Interpreting Test Results
Calculating Post-test Probabilities
Post-test Probabilities in Clinical Practice
Conclusions: Case Study 1
Part II
Part III
References


Chapter 14: Tools for Decision Making: Probability Theory
        

When a patient sits down in your office and says, "Doctor, I have chest pain," you begin a game of guessing what is going on under that person's skin. Since you can't be certain without doing something implausible like examining a potentially diseased organ with your naked eye, you must make a choice without being sure it is the right one.

Information can help you reduce your uncertainty. Probability theory is the language of uncertainty. It allows you to reduce your uncertainty about how uncertain you are about the cause of the patient's symptom!

When asked how sure they are of their diagnoses, most physicians express their degree of certainty in words. An alternative to using words is to use a number -- namely, the probability that the diagnosis is present.

Definition of Probability

A probability is a number between 0 and 1 expressing the likehood that an event will occur.

  • 0 represents certainty that it will not occur
  • 1 represents certainty that it will occur

In the case study that follows, you will see how probability can help reduce uncertainty in the medical decision making process.

Odds and probability are two equivalent ways to express uncertainty. This chapter moves back and forth between them often.

If you don't understand the relationship between the odds of disease and the probability of disease, click on the FYI box below.

FYI: Odds versus Probability

 

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