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Diagnostic Tests

Diagnostic Tests. Afshin Ostovar Bushehr University of Medical Sciences Bushehr, 2013. A normal individual is a person; Who has not been sufficiently examined. Introduction. It is necessary to distinguish between people in the population who have the disease and those who do not.

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Diagnostic Tests

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  1. Diagnostic Tests Afshin Ostovar Bushehr University of Medical Sciences Bushehr, 2013

  2. A normal individual is a person; Who has not been sufficiently examined

  3. Introduction • It is necessary to distinguish between people in the population who have the disease and those who do not. • clinical arena • Public health arena • How good is the test in separating populations of people with and without the disease in question?

  4. Biologic Variation of Human Populations Bimodal curve

  5. Unimodal curve

  6. Validity of screening Tests • Sensitivity: • the ability of the test to identify correctly those who have the disease. • Specificity: • the ability of the test to identify correctly those who do not have the disease.

  7. Tests with Dichotomous Results Gold Standard

  8. False Positive effects • Burden on the health care system • Labeling effect

  9. False Negative effects • Deprivation from effective treatment • It depends on: • the nature and severity of the disease • the effectiveness of available intervention measure • Whether the effectiveness is greater if the intervention is administered early in the natural history of the disease

  10. Tests of Continuous Variables Sensitivity= 5/20 = 25% Specificity= 18/20 = 90%

  11. Tests of Continuous Variables Sensitivity= 17/20 = 85% Specificity= 6/20 = 30%

  12. Different cut-offs

  13. Multiple Tests • Sequential (Two-stage) Testing • Simultaneous (Parallel) Testing

  14. Sequential (Two-stage) Testing

  15. Test 1

  16. Prevalence = 5%

  17. Sensitivity = 70%

  18. Specificity = 80%

  19. Final Result of Test 1

  20. Test 2

  21. Sensitivity= 90%

  22. Specificity=90%

  23. Final Result of two Tests Net sensitivity = 315/500 = 63% Net Specificity = 9310/9600 = 98%

  24. Simultaneous Testing

  25. Test A

  26. Test B

  27. Test A

  28. Test B

  29. Predictive Value • Predictive Value Positive • If the test results are positive in this patient, what is the probability that this patient has the disease. • Predictive Value Negative • If the test results are negative in this patient, what is the probability that this patient do not have the disease

  30. Predictive Value Positive

  31. The predictive value is affected by two factors: • the prevalence of the disease in the population tested • the specificity of the test being used, when the disease is infrequent.

  32. Relationship between Predictive Value and Disease Prevalence

  33. Relationship between Predictive Value and Disease Prevalence

  34. Relationship between Predictive Value and Disease Prevalence

  35. Relationship between Predictive Value and Specificity

  36. Relationship between Predictive Value and Specificity

  37. Relationship between Predictive Value and Specificity

  38. Relationship between Predictive Value and Specificity

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