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Diagnostic & Screening TestsDiagnostic and screening tests attempt to reveal an otherwisehidden truth about patients (i.e., their health status: diseasedor disease-free). •Physical examination •Radiographs/Computed Tomography (CT) •Blood and urine assays •Cytology (Paps smear, Oral brush biopsy) •Saliva (HIV testing)
Discrimination & Classification“The fundamental principle of diagnostic testing [andscreening] rests on the belief that individuals with disease aredifferent from individuals without disease and that diagnostic[and screening] tests can distinguish between these twogroups.” Riegelman, Studying a Study and Testing a Test, 2000 •Valid (i.e., accurate) Sensitivity, specificity, ROC Predictive values Multiple tests •Reliable (i.e., precise or repeatable) Percent agreement Kappa
Discrimination & Classification Disease status comes from an external source of “truth” regarding the patients in the population: •Gold standard or reference standard Adequate Independent Unbiased Representative
Interlude: The Gold StandardUnbiased • The procedure used to establish the truth should not bias the truth. • Surgery or histology the “truth” will consist of the more advanced casesRepresentative • Cadaver studies of TMJ (older). Patients younger. • Caries simulations (drilled holes in teeth) versus natural lesions
Interlude: The Gold StandardAdequate •Surgery or autopsy (common in imaging studies) •Time between imaging and surgery/biopsy •Applies to positive cases •Negative cases – clinical follow-upIndependent •Histology provides an independent truth. •Occasionally all of the available information, including the test being tested is used to establish the gold standard. Bone lesion for example (BFO). Creates a bias in favor of the test
Discrimination & Classification “Appearances to the mind are of four kinds. Things either are what they appear to be [ ]; or they neither are, nor appear to be [ ]; or they are, and do not appear to be [ ]; or they are not, and yet appear to be [ ]. Rightly to aim in all these cases is the wise man’s task.” Epictetus (c. 50-120) Discourses, Bk I, Chp 27
Validity: Sensitivity & SpecificitySensitivity = Ability of the test to correctly identify those with disease = Probability of testing positive given the presence of disease = TP / (TP + FN) = a / (a + c)
Validity: Sensitivity & SpecificitySpecificity = Ability of the test to correctly identify those without disease = Probability of testing negative given the absence of disease = TN / (FP + TN) = d / (b + d)
Validity: Sensitivity & SpecificityAssume a population of 1000 people of whom 100 have adisease. Of these 100 people, the test correctly identifies 80.Ofthe 900 disease-free people, the test correctly identifies 800. Sensitivity = a / (a + c) = 80 / 100 = 80% Specificity = d / (b + d) = 800/ 900 = 89% Gordis, 2009, Table 5-1
Validity: Sensitivity & SpecificitySensitivity and Specificity • Inherent characteristics of the test • Stable over different populations with different disease prevalence • Useful for comparing performance of two tests (e.g., Digital versus film mammography / Pisano, NEJM 2005) • Have a reciprocal relationship with one another
Validity: Sensitivity & Specificity Low cutoff High sensitivity Low specificity False positives Moderate cutoff balance High cutoff Low sensitivity High specificity False negatives Courtesy, S. Fleming, 2011
Validity: Receiver Operating Characteristic Curve X-axis: False positive ratio (1-specificity) Y-axis: True positive ratio (sensitivity)
Validity: Performance / Predictive ValueSensitivity and specificity are useful, but • May be numerically different if obtained on a group of people with early stages of disease compared with a group with more advanced disease. • We do not know ahead of time who has the disease and who does not. Rather, we get the test results and need to interpret the findings.
Validity: Performance / Predictive ValuePositive Predictive Value = Ability of the test to correctly identify those who test positive = Probability of having the disease given a positive test result = TP / (TP + FP) = a / (a + b)
Validity: Performance / Predictive ValueNegative Predictive Value = Ability of the test to correctly identify those who test negative = Probability of not having the disease (i.e., being disease-free) given a negative test result = TN / (FN + TN) = d / (c + d)
Validity: Positive & Negative Predictive ValuesAssume a population of 1000 people of whom 100 have adisease. Of these 100 people, the test correctly identifies 80.Ofthe 900 disease-free people, the test correctly identifies 800. Positive PV = a / (a + b) = 80 / 180 = 44% Negative PV = d / (c + d) = 800/ 820 = 98% Gordis, 2009, Table 5-7
Validity: Predictive Values & PrevalenceAssume a test with a sensitivity of 80% and specificitity of 90%.What happens to the predictive values when the prevalence ofthe disease varies? To fill in the cells, assume a convenient totalpopulation, in this case 1000. 80 90 20 810 Positive PV = a / (a + b) = 80 / 170 = 0.4706 = 47.1% Negative PV = d / (c + d) = 810/ 830 = 0.9759 = 97.6% After Kramer Clinical Epidemiology and Biostatistics, 1988
Validity: Predictive Values & PrevalenceAssume a test with a sensitivity of 80% and specificitity of 90%. Positive PV = a / (a + b) = 400 / 450 = 0.8888 = 88.9% Negative PV = d / (c + d) = 100/ 550 = 0.8181 = 81.8% After Kramer Clinical Epidemiology and Biostatistics, 1988
Validity: Predictive Values & PrevalenceAssume a test with a sensitivity of 80% and specificitity of 90%. Positive PV = a / (a + b) = 720 / 730 = 0.9863 = 98.6% Negative PV = d / (c + d) = 90/ 270 = 0.3333 = 33.3% After Kramer Clinical Epidemiology and Biostatistics, 1988
Validity: Predictive Values & PrevalenceAssume a test with a sensitivity of 80% and specificitity of 90%. Some additional terms: • Pretest probability = prior probability = prevalence • Post-test probability = posterior probability = positive/negative predictive value • Bayes Theorem (Thomas Bayes, 1702-61)
Multiple Tests: Simultaneous Suppose in a population of 1000 people, 200 have the disease and Test A sensitivity = 80% Test B sensitivity = 90% Net sensitivity = A+, B+ or both Step 1: 0.8 x 200 = 160 who are A+ Step *: 0.9 x 200 = 180 who are B+ Step 2: 0.9 x 160 = 144 who are A+B+ Step 3: 160 – 144 = 16 who are A+ only Step 4: 180 – 144 = 36 who are B+ only Step 5: 144 + 16 + 36 = 196 = A+,B+, or both Step 6: 196/200 = 98%Courtesy, S. Fleming, 2011
Multiple Tests: Simultaneous Suppose in a population of 1000 people, 800 don’t have the disease Test A specificity = 60% Test B specificity = 90% Net specificity = A- and B- Step 1: 0.6 x 800 = 480 who are A- Step *: 0.90 x 800 = 720 who are B- Step 2: 0.9 x 480 = 432 who are A- and B- Step 3: 432/800 = 54%Courtesy, S. Fleming, 2011
Reliability Reliability (aka repeatability or precision) is the ability of the test to give consistent results when performed more than once by on the same individual under the same conditions, even if conducted by different examiners. Sources of variability (the antithesis of repeatability) •Subjects BP reading (throughout day, sitting/standing, R/L arm) Serum glucose (throughout day, day of the week) •Instrumentations PSA assay (5% variability even when measuring identical blood sample) •Observer Intra-observer Inter-observer
Reliability: Percent Agreement Percent agreement = number of tests that agree / total number of tests = (a + d) / (a + b + c + d) = 35 / 40 = 0.875 = 87.5%
Reliability: KappaMeasure agreement beyond that expected from chance alone:Kappa = (percent agreement – chance agreement) (1 – chance agreement)Kappa varies between 0 (no agreement) and 1 (perfect agreement) < 0.40 Poor agreement 0.40 - 0.75 Fair to good agreement > 0.75 ExcellentIn example, chance agreement = 0.695Kappa = (0.875 – 0.695)/(1 – 0.695) = 0.180/0.305 = 0.590
Reliability: Kappa Kundel and Polansky, Radiology, 2003
Reliability: Calculating KappaTwo pathologists independently read and score 75histopathology slides using their own criteria to subtype thelesion as Grade II or Grade III Gordis, 2009, Figure 5-17
Screening“Screening is defined as the presumptive identification ofunrecognized diseasese or defects by the application of tests,examinations, or other procedures that can be applied rapidly.” Friis and Seller, 2009“For screening to be of benefit, treatment given during thedetectable preclinical phase must result in a better prognosisthan therapy given after symptoms develop.” Hennekens and Buring, 1987
ScreeningNature of the Disease •Important health problem Morbidity/Mortality •Treatable Unethical to screen if untreatable, except to prevent transmission (e.g., early cases of AIDS versus protecting blood supply) •Relatively high prevalence Rare disease PPV is low & cost per case detected is high Exceptions: Phenylketouria (PKU), 1 in 15,000 births, but consequences are severe (mental retardation), treatment is simple (dietary restriction), screening tests are simple. •Detectable preclinical phase (long latency period) Biological Symptoms Onset Appear Clinical Clinical Outcome Screening Diagnosis
ScreeningNature of the Test •Simple Easy to learn and perform No complicated patient preparation •Rapid To administer To yield results •Safe Screened populations are overwhelmingly healthy – keep them that way •Valid and reliable High sensitivity Relatively high specificity – accept some FP as there will be follow-up confirmatory tests, but what is the cost and morbidity of the follow-up, the cost of mislabeling someone, etc.
ScreeningSocietal Factors •Cost Relatively inexpensive Benefit/cost ratio favorable versus other health care expenditures •Acceptable Unpalatable or difficult tests refusal to participate
ResourcesLanglotz, Radiology 2003 – supplement to Gordis, especiallyfor ROC curves.Pisano et al. NEJM 2005 – example of an application ofconcepts.Linker, AJPH 2012 – and interesting historical perspective ofscreening, specifically for scoliosis.US Preventive Services Task Force (USPSTF) – the source ofmany guidelines (and some controversy) regarding screening:< http://www.uspreventiveservicestaskforce.org/>.