Medical Decision Making with Clinical Tests

The following analysis (Test Decision) provides an analytical framework on how to interpret clinical tests you may undertake.

Test Decision

At a high level, I can give you a summary of the whole concept. 

 

 

 

 

 

 

The main driver of the ultimate accuracy of your test result is the prevalence rate of the disease you are tested for given your specific demographics and health parameters.    

If given your specific circumstances, the disease you are being tested for has a high prevalence rate: 

1. If you receive a positive test, given reasonable sensitivity and specificity of that test, the test may be reasonably accurate.  And, you may be ready to move on to the treatment phase;
2. If you receive a negative test, there is probably a material likelihood that you are getting a false negative result.  And, you may want consider taking a second test to confirm this negative test result.   

If given your specific circumstances, the disease you are being tested for has a low prevalence rate, just the opposite is true as outlined below: 

1. If you receive a positive test, the test is likely to have generated a false positive.  And, you may want to consider taking a second test to confirm this positive test result;
2. If you receive a negative test, given reasonable sensitivity and specificity of that test, the test may be reasonably accurate.  And, you are probably fine.

The attached study details all the underlying Bayesian statistics associated with this decision protocol.  It also covers different situations when you undertake more than one test.