Presentation on theme: "Azita Kheiltash Social Medicine Specialist Tehran University of Medical Sciences Diagnostic Tests Evaluation."— Presentation transcript:
Azita Kheiltash Social Medicine Specialist Tehran University of Medical Sciences Diagnostic Tests Evaluation
Diagnostic Tests The term ′ diagnostic tests ′ includes everything physicians do to diagnose disease. This includes assessing symptoms and signs, as well as what we conventionally refer to as tests: such as laboratory investigations, imaging, …
Gold Standard The gold standard is the best single test (or a combination of tests) that is considered the current preferred method of diagnosing a particular disease (X). All other methods of diagnosing X, including any new test, need to be compared against this ′ gold ′ standard. The gold standard is different for different diseases.
Validity It is the extent to which a test measures what it is supposed to measure; in other words, it is the accuracy of the test. Validity is measured by sensitivity and specificity. The validity of a diagnostic test is the extent to which the results represent an unbiased estimate of the underlying truth (it is testing what it supposed to test)
Definitions Imagine a study evaluating a new test that screens people for a disease. The test results may be (comparing a new diagnostic test with the gold standard): True positive: Sick people correctly diagnosed as sick False positive: Healthy people incorrectly identified as sick True negative: Healthy people correctly identified as healthy False negative: Sick people incorrectly identified as healthy.
Sensitivity Sensitivity: The capacity of the test to correctly identify diseased individuals in a population “TRUE POSITIVES”. The greater the sensitivity, the smaller the number of unidentified case “false negatives”. Sensitivity relates to the test's ability to identify positive results. If a test has high sensitivity then a negative result would suggest the absence of disease. Sensitivity = TP/(TP + FN)
Specificity Specificity: The capacity of the test to correctly exclude individuals who are free of the disease “TRUE NEGATIVES”. The greater the specificity, the fewer “false positives” will be included. Specificity relates to the ability of the test to identify negative results. If a test has high specificity, a positive result from the test means a high probability of the presence of disease. Specificity = TN/(TN + FP)
Positive predictive value Positive Predictive Value: The probability of the disease being present, among those with positive diagnostic test results. The positive predictive value, or precision rate is the proportion of subjects with positive test results who are correctly diagnosed. It is a critical measure of the performance of a diagnostic method, as it reflects the probability that a positive test reflects the underlying condition being tested for. Positive predictive value (PPV) = TP/(TP + FP)
Negative predictive value Negative Predictive Value: The probability that the disease was absent, among those whose diagnostic test results were negative. The negative predictive value (NPV) is a summary statistic used to describe the performance of a diagnostic testing procedure. It is defined as the proportion of subjects with a negative test result who are correctly diagnosed. A high NPV means that when the test yields a negative result, it is uncommon that the result should have been positive. Negative predictive value (NPV) = TN/(TN + FN)
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