Updated: 2/9/2022

# Testing and Screening

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 Overview Diagnostic testing performance is measured in a variety of ways Sensitivity and specificity describe the frequency of test results by disease status Positive and negative predictive value describe the frequency of disease status by test result Precision and accuracy describe different types of variation in test results Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value These 4 measures describe how well diagnostic tests capture the true presence or absence of disease  Predictive value changes with disease prevalence, sensitivity and specificity do not A 2x2 contingency table can help with calculations  sensitivity (SN)   % with disease who test positive = a/(a+c) = TP/(TP+FN)  highly sensitive tests are good at ruling out disease (rule out SnOut) tests with high sensitivity are good for screening purposes  e.g., COVID-19 testing would benefit from high sensitivity so all potential cases can be isolated quickly, even if that means briefly isolating those who do not have the disease until follow-up test results return false negative rate = 1-sensitivity specificity (SP)  % without disease who test negative = d/(b+d) = TN/(FP+TN) highly specific tests are good at ruling in disease (rule in SpIn) tests with high specificity are good confirmatory tests e.g., after a patient screens positive for HIV on a rapid test, the confirmatory test should be highly specific to ensure that the person is not given a false positive diagnosis of a serious illness false positive rate = 1-specificity positive predictive value (PPV)        % positive test results that are true positives = a/(a+b) = TP/(TP+FP)  ↑ prevalence causes ↑ PPV negative predictive value (NPV)   % negative test results that are true negatives = d/(c+d) = TN/(FN+TN)  ↑ prevalence causes ↓ NPV  Cut-off point for positivity may be adjusted to optimize sensitivity and specificity for different purposes, which are inversely related (cut-off point with decreased sensitivity is associated with increased specificity and vice-versa)   Receiver operating characteristic (ROC) curves are a graphical depiction of a test's overall diagnostic performance Y axis sensitivity X axis 1-specificity the closer the curve fills out the top left corner, the better the test is performance is quantified by the area under the curve (AUC) an AUC of 0.5 states that the test performs no better than chance (bad test!) an AUC of 0.9 suggests a better-performing test Likelihood Ratios (LRs) Also used to assess diagnostic test performance in isolation or in sequence Does not change with disease prevalence Represents the probability of a patient with a disease having a positive or negative test result in comparison to the probability of a patient without the disease having a positive or negative test result Positive LR "How many times more likely is a positive test result observed in cases versus non-cases?" suggests how well disease is ruled in = probability of positive test in cases/probability of positive test in non-cases = true positive/false positive = sensitivity/1-specificity = [a/(a+c)]/[1-(d/(b+d))] or [a/(a+c)]/[b/(b+d)] positive LR > 1 suggests that patients with the disease are more likely to have a positive result compared to those without the disease Negative LR "How many times more likely is a negative test result observed in cases versus non-cases?" suggests how well disease is ruled out = probability of negative test in cases/probability of negative test in non-cases = false negative/true negative = 1-sensitivity/specificity = [1-a/(a+c)]/[d/(b+d)] or [c/(a+c)]/[d/(b+d)] negative LR < 1 suggests that patients with the disease are less likely to have a negative result compared to those without the disease
 Precision and Accuracy Precision    also known as reliability   consistent  reproducible no random variation Accuracy  reflects true value no systematic variation