Prior probability (the pretest best guess) affects predictive values of diagnostic tests.

01:12 EDT 30th June 2015 | BioPortfolio

Summary of "Prior probability (the pretest best guess) affects predictive values of diagnostic tests."

Authors who publish evaluations of dichotomous (yes/no) diagnostic tests often include the predictive values of their test at a single prior probability (eg, the prevalence of the target disease within the evaluation data set). The objectives of this technical note are to demonstrate why single-probability predictive values are misleading and to show a better way to display positive predictive values (PPV) and negative predictive values (NPV) for a newly evaluated test. Secondly, this technical note will show readers how to calculate predictive values from only sensitivity and specificity for any desired prior probability. As prior probability increases from 0% to 100%, PPV increases from 0% to 100%, but NPV goes in the opposite direction (drops from 100% to 0%). Because prior probabilities vary so greatly across situations, predictive values should be provided in publications for the full range of potential prior probabilities (if provided at all). This is easily done with a 2-curve graph displaying the predictive values (y-axis) against the prior probability (x-axis).


Section of Epidemiology, Department of Population Medicine & Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY, USA.

Journal Details

This article was published in the following journal.

Name: Veterinary clinical pathology / American Society for Veterinary Clinical Pathology
ISSN: 0275-6382


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Medical and Biotech [MESH] Definitions

In screening and diagnostic tests, the probability that a person with a positive test is a true positive (i.e., has the disease), is referred to as the predictive value of a positive test; whereas, the predictive value of a negative test is the probability that the person with a negative test does not have the disease. Predictive value is related to the sensitivity and specificity of the test.

Functions constructed from a statistical model and a set of observed data which give the probability of that data for various values of the unknown model parameters. Those parameter values that maximize the probability are the maximum likelihood estimates of the parameters.

A range of values for a variable of interest, e.g., a rate, constructed so that this range has a specified probability of including the true value of the variable.

The complete summaries of the frequencies of the values or categories of a measurement made on a group of items, a population, or other collection of data. The distribution tells either how many or what proportion of the group was found to have each value (or each range of values) out of all the possible values that the quantitative measure can have.

The probability that an event will occur. It encompasses a variety of measures of the probability of a generally unfavorable outcome.