In summary, this conversation discusses the various factors and terminology related to disease and virus testing. The frequency of false positives and false negatives is dependent not only on the tests themselves, but also on the prevalence of the disease or virus within the population. Proper analysis of the probabilities involved is important in interpreting test results. The conversation also mentions that in practical cases, some data may be unknown, leading to more advanced techniques such as hypothesis testing. It is assumed that there is a single test for a virus. The suggestion to include graphs to illustrate the relationship between prevalence and positive/negative predictive values is also mentioned.
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Introduction
This Insight looks at the various probabilistic factors and related terminology involved in disease and virus testing.
As we all know, tests are rarely 100% reliable.  The frequency of false positives and false negatives, however, not only depend on the tests themselves, but also on the prevalence of the disease or virus within the population.  To see this, imagine the two extremes where a) no one has the virus, and b) everyone has the virus.  In the first case, all positives must be false.  And, in the second, all negatives must be false.
This provides the motivation for doing a proper analysis of the probabilities involved to see more precisely what can be concluded from a test result given all the available data.
Note that this insight provides a simple probabilistic analysis.  In many practical cases, some or all of the data is unknown, which leads to the more advanced techniques of hypothesis testing.
We assume throughout that we have a single test for a virus...

Continue reading...
 
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Likes DaveE, Choppy, Astronuc and 5 others
  • #1
Very interesting, who knew testing was so complex!
 
  • #3
Nice writeup!
I think a couple of graphs would be useful though, like the PPV/NPV as a function of prevalence for a couple of values of p/q, to really drive the message home.
 
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1. What are probabilistic factors?

Probabilistic factors refer to the likelihood or probability of an event occurring. In the context of disease and virus testing, these factors can include the chance of a person being infected with a particular disease or virus, the accuracy of the test being used, and the prevalence of the disease or virus in a specific population.

2. How do probabilistic factors play a role in disease and virus testing?

Probabilistic factors are important in disease and virus testing as they help determine the accuracy and reliability of the test results. These factors can also influence the interpretation of the results and the potential risk of false positives or false negatives.

3. What are some examples of probabilistic factors in disease and virus testing?

Some examples of probabilistic factors in disease and virus testing include the sensitivity and specificity of the test, the prevalence of the disease or virus in a particular population, and the potential for human error in conducting the test or interpreting the results.

4. How do scientists account for probabilistic factors in disease and virus testing?

Scientists account for probabilistic factors in disease and virus testing by conducting thorough research and validation studies to determine the accuracy and reliability of the test. They also consider the prevalence of the disease or virus in the population being tested and use statistical methods to analyze and interpret the results.

5. Can probabilistic factors change over time in disease and virus testing?

Yes, probabilistic factors can change over time in disease and virus testing. This can be due to various factors such as changes in the accuracy of the test, the prevalence of the disease or virus in the population, and advancements in testing methods. It is important for scientists to regularly review and update their understanding of these factors to ensure the most accurate and reliable testing methods are being used.

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