The terms sensitivity, specificity, positive and negative predictive value constitute the statistical quality criteria of diagnostic testing. You should not only know their meaning and interpretation while preparing for your written exams. If you want to make (responsible) treatment decisions as a doctor, there is no way around these parameters. Incidence rate and pretest probability are further parameters that you need to be familiar with. This article will provide you with an overview of various statistical quality criteria and their interpretation.
diagnostic Testing

Image: “Malaria rapid diagnostic test.” by Nicole Schiegg/USAID. License: Gemeinfrei

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Diagnostic Testing

In medicine, a diagnostic test examines whether a disease or a particular characteristic is present or not. Examples are tests for metabolic diseases during a new-born screening, HIV testing, or even a simple pregnancy test. Broadly speaking, there is a distinction between a screening test and a confirmatory test. The former should ideally be applied to all sufferers (high sensitivity), but because of this, it is also more likely to yield a false positive test result.

The confirmatory test is often performed after a screening test in order to guarantee its result. This means that only those people who are actually sick will be classified as such (high specificity). Now, what exactly do the individual terms mean?

new-born screening heel

Image: “The blood of a two week-old infant is collected for a Phenylketonuria, or PKU, screening.” by U.S. Air Force photo/Staff Sgt Eric T. ShelerUSAF Photographic Archives. License: in the public domain.

Statistical Quality Criteria of Diagnostic Tests

Sensitivity and specificity

The sensitivity of a diagnostic procedure gives us information as to what percentage of sufferers is actually recognized as sick by the test method. The sensitivity thus describes the “true positive results“. For example, the Ebola rapid test (ReEBOV test) by the company Corgenix (USA) has a sensitivity of almost 100 %. In this sense, a patient suffering from Ebola is properly diagnosed in all cases with this test.


Image: “Sensitivität” by Nichtich, MartinGarbe. License: in the public domain.

Image: A test should distinguish between sick and healthy people. Every person is represented by a dot which lies either to the left (ill) or to the right (healthy) of the black line. The dots in the oval are those people that have been classified as sick by the test.

Specificity indicates the probability at which a medical test method for a given disease will recognize that a healthy person is also actually healthy. With a specificity of 92 %, our Ebola rapid test of 100 healthy patients detects on average 92 people as healthy (“true negative rate“). Eight healthy individuals are therefore erroneously diagnosed as having Ebola, even though they are healthy, and may have to endure quarantine measures as a result.


Image: “Spezifität” by Nichtich, MartinGarbe. License: in the public domain.

Positive predictive value

The positive predictive value is the probability of how many people, who had a positive medical test, are actually ill (right = positive). The positive predictive value is also dependent on the prevalence of the disease in the population. With a more common disease, the likelihood of actually being affected is a lot higher than with very rare diseases and a positive value.

Negative predictive value

The negative predictive value indicates how many people, whose medical test result turned out negative, are actually healthy. It also depends on the prevalence of the disease in the population.


Prevalence is an epidemiological measure for disease frequency. It indicates how many people are suffering from a particular disease. The value refers to a sample that is generalized extrapolated to the population or to actual numbers.

While relatively accurate data is available for notifiable diseases, others can only be representatively “extrapolated” through sampling. The prevalence thus describes a current state: How many people in 2015, for example, suffer from AIDS, regardless of when they came down with the disease.

The Fourfold Table

The fourfold table combines the just mentioned test quality criteria in one model, and you will encounter it often as a medical student. In general and social medicine you may find yourself in the situation of having to calculate the positive predictive value or sensitivity.

If the meaning of the parameters is clear, the calculation is actually quite simple. If you have given values for the quality criteria of certain tests, you can illustrate the ratios of the parameters based on numerical examples.

Positive Test Negative Test Total Test Results   Calculation
Ill a (true positive) b (false negative) a + b (all sufferers) Sensitivity = a / (a + b)
Healthy c (false positive) d (true negative) c + d (all healthy) Specificity = d / (c + d)
Total Persons a + c (all positive tests) b + d (all negative tests)
Calculation Positive predictive value = a / (a + c) Negative predictive value = d / (b + d)

Example: Suppose that 7 % of the population in an affected area (this is freely chosen, as there are no reliable numbers regarding this) is suffering from Ebola. We are testing all the people in our small clinic. Altogether, 1,000 people are examined with the Ebola-rapid test in order to detect outbreaks early and be able to isolate the affected persons.

Positive test  Negative test Total Test Results Calculation
Ill  70 0 70 (7 % prevalence) Sensitivity = 100 %
Healthy  65 865 930 (100 – 7 = 93 % Healthy) Specificity = 92 %
Total Persons 135 865 1.000 (tested persons)
Calculation Positive predictive value = 0,52 Negative predictive value = 1


Out of 1,000 tested persons, 70 people (7 %) are actually infected by the virus and their test is positive. Because of the lower specificity of the rapid test, 65 of the healthy 930 people also get a positive Ebola test result. Out of a total of 135 Ebola tests with positive result, almost half, 65 people in our hospital, will have been diagnosed with a false positive and had to be treated for safety reasons and put in isolation. Despite the apparently high values, the chance to be truly suffering from Ebola with a positive test result, in our example, is only 52 %!

Such considerations become important in the medical profession in the case of well-known screening tests, such as mammography for breast cancer or the PSA level determination for prostate cancer prevention. False positive test may result in uncertainty and invasive diagnosis for the patient. Therefore, it is useful to be able to deal with test results and probabilities in order to optimally inform patients and to plan further diagnosis and treatment steps.

Other Statistical Values


Incidence is the number of new cases in a defined period of time. This is usually specified as the number of new cases / 100,000 inhabitants and is called the incidence rate.

Pretest probability

The pretest probability indicates how likely a test will yield a positive result. Therefore, it is dependent on the prevalence. For example, the pretest probability for our Ebola rapid test is much lower in East Africa than Sierra Leone due to a much lower prevalence.

At a Glance: Statistical Principles

  • Sensitivity: How many sufferers are “true positive” and considered truly ill?
  • Specificity: How many healthy people are considered “true negative” (healthy) by the test?
  • Positive predictive value: Positive test result and truly ill.
  • Negative predictive value: Negative test result and truly healthy.
  • Prevalence: How many people are suffering from a particular disease in the XY time period?
  • Incidence: How many people fall ill from a particular disease during the XY period?
  • Pretest probability: How likely is the test to be positive? Prevalence – influence!

Popular Exam Questions on Statistical Principles in Epidemiology and General Medicine

The answers are listed below the references.

1. Out of 100,000 inhabitants, 346 people are suffering from a particular disease in 2014 in Germany. This number statistically corresponds to…

  1. …prevalence.
  2. …the positive predictive value.
  3. …the incidence rate.
  4. …the negative predictive value.
  5. …the sensitivity.

2. Which statistical parameter corresponds to the number of true positive test results?

  1. Positive predictive value
  2. Sensitivity
  3. Specificity
  4. Negative predictive value
  5. None of the above

3. Prevalence is a parameter for…

  1. …incidence rate.
  2. …mortality rate.
  3. …birth rate.
  4. …disease frequency.
  5. …probability of a positive test result.
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