True positive cases are detected by

Ans. b (Sensitivity). (Ref. Park, PSM, 17th/pg. 112)TERMDEFINATIONSensitivityThe probability that a person who actually has the disease of interest will have a positive (abnormal) test result. In clinical practice sensitivity is inversely proportional to specificity.SpecificityThe probability that a person who actually does not have the disease of interest will have a negative (normal) test result.PrevalenceThe proportion of persons in a given population who have a particular disease at a point or interval of time.False-positiveA test result that is normal (positive) despite the true absence of the disease of interest or a study result that incorrectly suggests an effect, when in truth, the purported effect does not exist (see also Type 1 error).True-negativeA test result that is normal (negative) when the disease of interest is actually absent.True-positiveA test result that is abnormal (positive) when the disease of interest is actually present.False-negativeA test result that is normal (negative) despite the true presence of a particular disease or a study result that incorrectly fails to identify a true effect (see also Type II error). Does not have the disease of interest.Positive predictive valueThe probability that a person with a positive (abnormal) test result actually has the disease of interestNegative predictive valueThe probability that a person with a negative (normal) test result actually. Screening test resultsDiagnosis diseasedNot diseasedTotalPositivea(true positive)b(false positive)a + bNegativecffalse neg.)d(true neg.)C+dTotala+cb+da+b+c+da) Sensitivity = a/(a+c) x 100b) Specificity = d/(b+d) x 100c) Predictive value of a positive test = a/(a+b) x 100d) Predictive value of a neg. test = d/(c+d) x 100e) Percentage of false neg. = c/(a+c) x 100f) Percentage of false positive = b/(b+d) x 100