diagnostic testing 2009
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1
Medical Epidemiology
Interpreting Medical Tests and Other Evidence
2
Developmental characteristics: test parameters
2 types of Error: False Positive and False Negative
Disease Yes (D+) No (D-) Total
Positive (T+) a b a+b Test Negative (T-) c d c+d
Total a+c b+d n
3
Developmental characteristics: test parameters
Complements of error rates as desirable test propertiesSensitivity = Pr(T+|D+) = a/(a+c)
Sensitivity is PID (Positive In Disease) [pelvic inflammatory disease]
Specificity = Pr(T-|D-) = d/(b+d)
Specificity is NIH (Negative In Health) [national institutes of health]
Disease Yes (D+) No (D-) Total
Positive (T+) a b a+b Test Negative (T-) c d c+d
Total a+c b+d n
4
Typical setting for finding Sensitivity and Specificity Best if everyone who gets the new test also
gets “gold standard” What is a “gold standard”?
The perfect test, the truth Doesn’t happen Even reverse doesn’t happen Not even a sample of each (case-control
type) Case series of patients who had both tests
5
Setting for finding Sensitivity and Specificity Sensitivity should not be tested in
“sickest of sick” Should include spectrum of disease Specificity should not be tested in
“healthiest of healthy” Should include similar conditions.
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Developmental characteristics: Cut-points and Receiver Operating Characteristic (ROC)
Healthy
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Developmental characteristics: Cut-points and Receiver Operating Characteristic (ROC)
Healthy Sick
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Developmental characteristics: Cut-points and Receiver Operating Characteristic (ROC)
Fals pos= 20% True pos=82%
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Developmental characteristics: Cut-points and Receiver Operating Characteristic (ROC)
Fals pos= 9% True pos=70%
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Developmental characteristics: Cut-points and Receiver Operating Characteristic (ROC)
F pos= 100% T pos=100%
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Developmental characteristics: Cut-points and Receiver Operating Characteristic (ROC)
F pos= 50% T pos=90%
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Developmental characteristics: Cut-points and Receiver Operating Characteristic (ROC)
Receiver Operating Characteristic (ROC)
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Developmental characteristics: Cut-points and Receiver Operating Characteristic (ROC)
Receiver Operating Characteristic (ROC)
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Receiver Operating Characteristic (ROC)
ROC Curve allows comparison of different tests for the same condition without (before) specifying a cut-off point.
The test with the largest AUC (Area under the curve) is the best.
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Clinical Interpretation: Predictive Values
Most test positives below are sick. But this is because there are as many sick as healthy people overall. What if fewer people were sick, relative to the healthy?
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Clinical Interpretation: Predictive Values
Now most test positives below are healthy. This is because the number of false positives from the larger healthy group outweighs the true positives from the sick group. Thus, the chance that a test positive is sick depends on the prevalence of the disease in the group tested!
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Clinical Interpretation: Predictive Values
•the chance that a test positive is sick, as well as the chance that a test negative is healthy, are what a physician needs to know.
These are not sensitivity and specificity!
The numbers a physician needs to know are the predictive values of the test.
20
Clinical Interpretation: Predictive Values
Sensitivity (Se)
Pr{T+|D+}
true positives
total with the disease
Positive Predictive Value (PV+, PPV)
Pr{D+|T+}
true positives
total positive on the test
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Positive Predictive Value Predictive value positive The predictive value of a positive test. If I have a positive test, does that mean I have the
disease? Then, what does it mean? If I have a positive test what is the chance
(probability) that I have the disease? Probability of having the disease “after” you have a
positive test (posttest probability) (Watch for “OF”. It usually precedes the denominator
Numerator is always PART of the denominator)
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Clinical Interpretation: Predictive Values
T+
D+
T+andD+
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Clinical Interpretation: Predictive Value
Specificity (Sp)
Pr{T-|D-}
true negatives
total without the disease
Negative Predictive Value (PV-, NPV)
Pr{D-|T-}
true negatives
total negative on the test
24
Negative Predictive Value
Predictive value negative If I have a negative test, does that mean
I don’t have the disease? What does it mean? If I have a negative test what is the
chance I don’t have the disease? The predictive value of a negative test.
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Mathematicians don’t Like PV-
PV- “probability of no disease given a negative test result”
They prefer (1-PV-) “probability of disease given a negative test result”
Also referred to as “post-test probability” (of a negative test)
Ex: PV- = 0.95 “post-test probability for a negative test result = 0.05”
Ex: PV+ = 0.90 “post-test probability for a positive test result = 0.90”
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Where do you find PPV?
Table?
NO Make new table Switch to odds
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Use This Table ? NO Disease
Test Result
+ - Total
+ 95 8 103 - 5 92 97
Total 100 100 200 You would conclude that PPV is 95/103 = 92%
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Make a New Table
Disease Test
Result + - Total
+ 95 72 167 - 5 828 833
Total 100 900 1000
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Make a New Table
Disease Test
Result + - Total
+ 95 72 167 - 5 828 833
Total 100 900 1000 Probability of having the disease before testing was 10%. (pretest probability prevalence) Posttest probability (PPV) = 95/167 = 57% So we went up from 10% probability to 57% after having a positive test
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Switch to Odds
1000 patients. 100 have disease. 900 healthy. Who will test positive?
Diseased 100__X.95 =_95Healthy 900 X.08 = 72
We will end with 95+72= 167 positive tests of which 95 will have the disease
PPV = 95/167
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From pretest to posttest odds
Diseased 100 X.95 =_95
Healthy 900 X.08 = 72 100 = Pretest odds
900 .95 = Sensitivity__ = prob. Of pos test in dis
.08 1-Specificity prob. Of pos test in hlth
95 =Posttest odds. Probability is 95/(95+72)
72
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Remember to switch back to probability
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What is this second fraction?
Likelihood Ratio Positive Multiplied by any patient’s pretest odds
gives you their posttest odds. Comparing LR+ of different tests is
comparing their ability to “rule in” a diagnosis.
As specificity increases LR+ increases and PPV increases (Sp P In)
34
Clinical Interpretation: likelihood ratios
Likelihood ratio
LR+ = Sensitivity/(1-Specificity)
LR- = (1-Sensitivity)/Specificity
35
Clinical Interpretation: Positive Likelihood Ratio and PV+
O = PRE-TEST ODDS OF DISEASE
POST-ODDS (+) = O x LR+ =
YSPECIFICIT - 1
YSENSITIVIT x O
ODDS(+)POST-+1
ODDS(+)POST- = PPV = PV+
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Likelihood Ratio Negative
Diseased 100_ X.05 =_5__Healthy 900 X.92 = 828
100 = Pretest odds 900 .05 = 1-sensitivity = prob. Of neg test in dis
.92 Specificity prob. Of neg test in hlth(LR-)
Posttest odds= 5/828. Probability=5/833=0.6% As sensitivity increases LR- decreases and NPV
increases (Sn N Out)
37
Clinical Interpretation: Negative Likelihood Ratio and PV-
POST-ODDS (-) = O x LR- =
YSPECIFICIT
YSENSITIVIT-1 x O
38
Remember to switch to probability and also to use 1 minus
39
Post test probability given a negative test
= Post odds (-)/ 1+ post odds (-)
ODDS(-)POST-+1
ODDS(-)POST- -1= NPV = PV-
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Value of a diagnostic test depends on the prior probability of disease
Prevalence (Probability) = 5%
Sensitivity = 90% Specificity = 85% PV+ = 24% PV- = 99% Test not as useful
when disease unlikely
Prevalence (Probability) = 90%
Sensitivity = 90% Specificity = 85% PV+ = 98% PV- = 49% Test not as useful
when disease likely
41
Clinical interpretation of post-test probability
Don't treat for disease
Do further diagnostic
testingTreat for disease
Probability of disease:
0 1
Testing threshold
Treatment threshold
Disease ruled out
Disease ruled in
42
Advantages of LRs
The higher or lower the LR, the higher or lower the post-test disease probability
Which test will result in the highest post-test probability in a given patient?
The test with the largest LR+ Which test will result in the lowest post-test
probability in a given patient? The test with the smallest LR-
43
Advantages of LRs
Clear separation of test characteristics from disease probability.
44
Likelihood Ratios - Advantage
Provide a measure of a test’s ability to rule in or rule out disease independent of disease probability
Test A LR+ > Test B LR+– Test A PV+ > Test B PV+ always!
Test A LR- < Test B LR-– Test A PV- > Test B PV- always!
45
Using Likelihood Ratios to Determine Post-Test Disease Probability
Pre-test probability of disease
Pre-test odds of disease
Likelihood ratio
Post-test odds of disease
Post-test probability of disease
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Predictive Values
Alternate formulations:Bayes’ Theorem
PV+ =
Se Pre-test Prevalence
Se Pre-test Prevalence + (1 - Sp) (1 - Pre-test Prevalence)
High specificity to “rule-in” disease
PV- =
Sp (1 - Pre-test Prevalence)
Sp (1 - Pre-test Prevalence) + (1 - Se) Pre-test Prevalence
High sensitivity to “rule-out” disease
48
Clinical Interpretation: Predictive Values
PV+ And PV-1 Of Electrocardiographic Status2
For Angiographically Verified3 Coronary ArteryDisease, By Age And Sex Of Patient
Sex Age PV+ (%) PV- (%)
F <40 32 88F 40-50 46 80F 50+ 62 68
M <40 62 68M 40-50 75 54M 50+ 85 38
1. Based on statistical smoothing of results from 78 patients referred to NCMemorial Hospital for chest pain. Each value has a standard error of 6-7%.
2. At least one millivolt horizontal st segment depression.3. At least 50% stenosis in one or more main coronary vessels.
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Clinical Interpretation: Predictive Values
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If Predictive value is more useful why not reported?
Should they report it? Only if everyone is tested. And even then. You need sensitivity and specificity from
literature. Add YOUR OWN pretest probability.
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So how do you figure pretest probability? Start with disease prevalence. Refine to local population. Refine to population you serve. Refine according to patient’s presentation. Add in results of history and exam (clinical
suspicion). Also consider your own threshold for testing.
52
Pretest Probability: Clinical Significance Expected test result means more than
unexpected. Same clinical findings have different
meaning in different settings (e.g.scheduled versus unscheduled visit). Heart sound, tender area.
Neurosurgeon. Lupus nephritis.
53
What proportion of all patients will test positive?
Diseased X sensitivity
+ Healthy X (1-specificity) Prevalence X sensitivity +
(1-prevalence)(1-specificity) We call this “test prevalence” i.e. prevalence according to the test.
54
Combination tests: serial and parallel testing
Combinations of specificity and sensitivity superior to the use of any single test may sometimes be achieved by strategic uses of multiple tests. There are two usual ways of doing this.
Serial testing: Use >1 test in sequence, stopping at the first negative test. Diagnosis requires all tests to be positive.
Parallel testing: Use >1 test simultaneously, diagnosing if any test is positive.
55
Serial Testing
Doing the tests sequentially, instead of together with the same decision rule, is a cost saving measure.
This strategy – increases specificity above that of any of
the individual tests, but – degrades sensitivity below that of any of
them singly. Serial test to rule-in disease
56
Combination tests: parallel testing
Parallel Testing Usual decision strategy diagnoses if
any test positive. This strategy – increases sensitivity above that of any of
the individual tests, but – degrades specificity below that of any
individual test.Parallel test to rule-out disease
57
Clinical settings for parallel testing Parallel testing is used to rule-out
serious but treatable conditions (example rule-out MI by CPK, CPK-MB, Troponin, and EKG. Any positive is considered positive)
58
Clinical settings for serial testing
When treatment is hazardous (surgery, chemotherapy) we use serial testing to raise specificity.(Blood test followed by more tests, followed by imaging, followed by biopsy).
59
Typical setting for finding Sensitivity and Specificity Best if everyone who gets the new test
also gets “gold standard” Doesn’t happen Even reverse doesn’t happen Not even a sample of each (case-
control type) Case series of patients who had both
tests
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EXAMPLE
Patients who had both a stress test and cardiac catheterization.
So what if patients were referred for catheterization based on the results of the stress test?
Not a random or even representative sample.
It is a biased sample.
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If the test is used to decide referral for gold standard?
Disease No Disease
Total
Test Positive
95 72 167
Test Negative
5 828 833
Total 100Sn95/100 =.95
900Sp 828/900 = .92
1000
63
If the test is used to decide referral for gold standard?
Disease No Disease
Total
Test Positive
95
85
72
65
167
167150
Test Negative
5
1
828
99
833
833 100
Total 100
86Sn85/86=.99
900
164Sp 99/164=.4
1000
64
If the test is used to decide referral for gold standard?
Disease No Disease
Total
Test Positive
85 65 150
Test Negative
1 99 100
Total 86Sn85/86=.99
164Sp 99/164=.4
250
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