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Evaluation of diagnostic tests (view source)
Revision as of 12:31, 19 January 2011
, 12:31, 19 January 2011→Evaluating diagnostic tests
Sensitivity and specificity are the likelihood that animal’s are correctly classified as positive or negative, respectively. As such, sensitivity is calculated by dividing the number of infected animal that correctly tested positive during testing by the number of diseased animals. Specificity is calculated by dividing the number of non-disease animal’s that tested negative during testing by the total number of non-diseased animals. Calculations of sensitivity and specificity require that the true disease status of the animal is known i.e. a gold standard is available. A gold standard is a test with 100% sensitivity and specificity and is very rare therefore the true disease status of the animal often has to be estimated using a test with high sensitivity/specificity. <br /><br />
Sensitivity and specificity are the likelihood that animal’s are correctly classified as positive or negative, respectively. As such, sensitivity is calculated by dividing the number of infected animal that correctly tested positive during testing by the number of diseased animals. Specificity is calculated by dividing the number of non-disease animal’s that tested negative during testing by the total number of non-diseased animals. Calculations of sensitivity and specificity require that the true disease status of the animal is known i.e. a gold standard is available. A gold standard is a test with 100% sensitivity and specificity and is very rare therefore the true disease status of the animal often has to be estimated using a test with high sensitivity/specificity. <br /><br />
:''Sensitivity = A/(A+C)''<br />
:''Sensitivity = A/(A+C)''<br />
:''Specificity = D/(B+D)<br /><br />''
:''Specificity = D/(B+D)<br /><br />''
The proportion of false negative results that the test is expected to give can be calculated by 1 minus the sensitivity and the proportion of false positive results expected using the test can be calculated by 1 minus the specificity. <br />
The proportion of false negative results that the test is expected to give can be calculated by 1 minus the sensitivity and the proportion of false positive results expected using the test can be calculated by 1 minus the specificity. <br />
'''Predictive values''' are the probability that an individual’s test result reflect their true disease status. A '''positive predictive value (PV+)''' is the probability that an animal with a positive test result truly has the disease and '''negative predictive value (PV-)''' is the probability that an animal with a negative test result is truly free from disease. Predictive values are not only dependent on the characteristics of the test but also on the prevalence of the disease in the population.<br /><br />
'''Predictive values''' are the probability that an individual’s test result reflect their true disease status. A '''positive predictive value (PV+)''' is the probability that an animal with a positive test result truly has the disease and '''negative predictive value (PV-)''' is the probability that an animal with a negative test result is truly free from disease. Predictive values are not only dependent on the characteristics of the test but also on the prevalence of the disease in the population.<br /><br />
:''PV+ = A/(A+B)''<br />
:''PV- = D/(C+D)''<br /><br />
As the prevalence of the disease in the population increases animals are more likely to have the disease. Therefore the probability that an animal which tests positive truly has the disease increases i.e. PV+ increases and the probability that an animal which tests negative has the disease decreases i.e. PV- decreases. If the disease is rare and there is a low prevalence in the population animal's are less likely to have the disease, therefore the likelihood that an animal which tests positive is truly positive is low i.e. PV+ is low and the likelihood that an animal which tests negative does not have the disease is high i.e. PV- is high.
==Pre- and post-test probability==
Before performing a diagnostic test a veterinarian usually has an idea of the likelihood that an animal has a disease, usually based on the levels of the disease in the population. The probability that an animal has a disease before a diagnostic test is performed is termed the ===pre-test probability=== and is usually the prevalence of the disease in the population, but can be modified depending on other factors e.g. whether the animal is showing symptoms of disease, whether certain risk factors for disease are present. Once a diagnostic test has been performed this probability can be modified to incorporate the results of the diagnostic tests to give an overall probability that an animal has the disease i.e. '''post-test probability.''' This is carried out as follows:
''Pre-test probability = prevalence(P)''<br />
''Pre-test odds = p/(1-p)''<br />
''Post-test odds = pre-test odds X LR'' <br />
''Post-test probability = post-test odds/(1+post-test odds)''
==Selecting a cut-off for diagnostic tests==
==Selecting a cut-off for diagnostic tests==