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Diagnostic Testing

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Title: Diagnostic Testing


1
  • Diagnostic Testing
  • Brian Gage, MD
  • October 7, 2009
  • DOC Research
  • Acknowledgment http//www.cebm.utoronto.ca/
    glossary/spsncriteria.htm

2
Goals
  • To use terms used to describe diagnostic tests
  • To understand concepts pertaining to Dx tests
  • To set up a 2 x 2 table in Excel to do the math
  • To understand key principles when designing a
    study re a Dx test

3
2 x 2 Table
4
Accuracy
  • Sensitivity
  • the proportion of diseased persons who have a
    positive test
  • also called the true positive rate and can be
    calculated from a/(ac)
  • Specificity
  • the proportion of non-diseased persons who have a
    negative test
  • also called the true negative rate and can be
    calculated from d/(bd)

5
Why arent Sensitivity Specificity Affected by
Disease Prevalence?
6
SpIn SnOut
  • Specificity Remember SpPin
  • When a test has a high Specificity (i.e. few
    false positive, a Positive test rules IN the
    disorder.
  • Sensitivity Remember SnNout
  • When a test has a high Sensitivity, a
    Negative result rules OUT the disorder.

7
Diagnostic Accuracy
  • How would you evaluate the accuracy of 4
    commercial platforms that can genotype for 2
    genes (VKORC1, CYP2C9)?
  • You have existing DNA genotyping platforms
  • What are the most important parts of your study
    design?

8
Accuracy results (95 CI) for CYP2C9 and VKORC1
on 112 DNA samples
C. King et al. Am J Clin Path 2008
9
Calculating 95 Confidence Intervals (CI) with 0
or 100 Results
  • Suppose our Dx test was correct 95/100?
  • Then, we could calculate the 95 CI
  • Formula gt More functions gt Statistical gt Binomial
  • CRITBINOM(100,0.95, 0.025) 90
  • CRITBINOM(100,0.95, 0.975) 99
  • 89-98 calculated at http//faculty.vassar.edu/lo
    wry/prop1.html
  • Normal approximation p zSQRT(p(1-p)/n)
  • i.e., 95 1.96sqrt((.95.05)/100) 91-99
  • 89-98 calculated in SAS (PROC FREQ)
  • What if our test was perfect (i.e.,100/100)?
  • Excel wont calculate it the website gets
    (96-100).

10
95 CI for a perfect test Rule of 3
  • The upper 95 CI is obviously 100.
  • Calculate the lower 95 CI using the Rule of 3
  • Lower limit 3 failures/ trials
  • 97 in this case.
  • What is the 95 CI if 1000 out of 1000 test
    results were correct?

11
Predictive Values
  • Positive Predictive Value
  • The proportion of patients with a positive test
    who have the disease
  • Also known as post-test probability or
    posterior probability following a positive
    test.
  • Negative Predictive Value
  • The proportion of patients with a negative test
    who dont have the disease.
  • Are predictive values affected by prevalence?

12
What is the formula for PPV?
13
Odds
  • Odds P/(1-P) where P is the probability
  • E.g. If the probability is 25 what are the odds?
  • P O/ (O1)
  • E.g. If the odds are 1/3, what is the probability?

14
Does Baby J. have M.D.?
  • In a population of 100,020 we expect 20 true
    positives, but
  • Of the 100,000 without M.D.
  • Specificity 99.98 False rate 0.02
  • 99,980 will be true negative 20 will be false .

15
The post test odds are 11
  • Therefore,
  • Out of 100,020 infants, 20 will be truly positive
    and 20 will be false positive
  • Positive predictive value 50
  • An unselected baby boy with a positive screening
    test only has a 50/50 chance of having this rare
    disease.

16
Example PPV of pap smears?
  • Rate of atypia in healthy women is 1 out of 1000.
  • Sensitivity 0.70
  • Specificity 0.90

Find probability that a woman will have atypical
cervical cells given that she had a positive pap
smear.
17
Example Pap Screening for Cervical CAAdapted
from www.stat.psu.edu/ljs_05
18
Example Pap Screening for Cervical CAAdapted
from www.stat.psu.edu/ljs_05
19
Example Pap smear
20
Example Pap smear
21
PAP predictive value
  • PPV 70/10,060 0.00696 0.7
  • NPV 89,910/89,940 0.999

A healthy woman with a positive pap has tiny
chance (0.7) of truly having disease, while a
healthy woman with negative pap almost certainly
will be disease free.
22
Antman E. et al. N Engl J Med 19963351342
23
Example Cardiac Troponin I to Dx MI
  • Hospital B has decreased the threshold of a
    test to 0.2 ng/mL
  • How will this change effect
  • the of R/O MI pts. who now rule in?
  • a quality indicator of post-MI mortality?
  • a quality indicator of LOS?
  • a quality indicator of post-MI beta-blocker use?
  • Medicare reimbursement?

24
Example You are contacted to help design a
study of a D-Dimer test
  • Background Plasma D-Dimer is a fibrin
    degradation product (FDP) resulting from
    activation of coagulation and fibrinolysis
  • Accuracy of Acme Cardiac D-Dimer
  • Area under the ROC curve (sens. vs. 1-spec) 0.89
  • Intra-assay reproducibility, CV 12
  • Coefficient of Variation SD/mean
  • Sensitivity (95) specificity 50 because of
    FPs w/ CA, inflammation, surgery, etc.
  • FDA approved for measurement of D-Dimer

25
Question D-Dimer
  • D-Dimer is less accurate, but much faster and
    cheaper than Doppler LE, spiral CT, V/Q scan, or
    angiogram
  • If using the D-Dimer test to Dx a DVT or PE,
    should you target patients with a low, medium, or
    high pretest probability of disease?
  • Could use the pretest probabilities of PE from
    PIOPED, Table 6 9, 30, 68
  • Or use the pretest probabilities from Wells
    clinical prediction rule for DVT 3, 17, 75
  • Wells PS et al. Value of assessment of pretest
    probability of deep-vein thrombosis in clinical
    management. Lancet 19973501796

26
Question D-Dimer
  • Question How would you design the study to
    determine whether availability of the D-Dimer
    test in the ER or outpatient setting reduced the
    use of Doppler LE, V/Q scans, and/or spiral CT?

27
D-Dimer Results Goldstein N. et al. Arch Int Med
2001
28
More Results Goldstein N. et al. 2001
29
If You Dont Have a Gold Standard
Two Classes of Evidence for Validity
  • 1. Criterion Validity

2. Construct Validity - Define a model and
collect data to test that model. e.g. vitamin K
supplementation and carboxylation of proteins
with certain base pairs (CALU, MGP) e.g.
warfarin Rx and change in levels of assay.
  • Concurrent(Fuchs et al. 2003) used simultaneous
    reading comprehension to validate a reading grade
    level test
  • Eg. Troponin I correlates with CPK MB and ECG
    change
  • Predictive
  • E.g. drivers test and prediction of MVA

30
Reliability
  • Inter-rater Reliability Correlations between
    users
  • Intra-rater Reliability Correlation w/ same
    users
  • Why we have to be careful w/ use of correlation
    in this context?

31
Other Factors Influence the Clinical Decision to
Use a Diagnostic Test
  • v Pretest probability of disease
  • v Test sensitivity and specificity
  • Test costs (clinical and financial)
  • Treatment risks and benefits

32
Determining the Optimal Use of Diagnostic Tests
for Patients with Acute Respiratory Infections
Probability of Specific Pathogen
0
100
X
Y
Diagnostic Test
Pathogen Rx
Alternative Dx
No Test/Test
Test/Treat
Adapted from Pauker and Kassirer. NEJM. 1980
33
When Studying a Test
  • Calculate the reliability/reproducibility
  • Intra- and inter-observer and intra- and interlab
  • CV, Kappa to measure concordance of categorical
    vars, intraclass correlation coefficient (ICC)
    for cont. vars.
  • Calculate the validity/accuracy
  • Sensitivity, Specificity, PPV, NPV often require
    a gold standard
  • May need to use rule of 3 to calculate 95 CI
    of accuracy.
  • E.g. if you have 100 sensitivity w/ 20 patients,
    the upper limit of the 95 CI 3/20 or 15.
  • Study design principles still hold
  • Blinded assessment An accepted gold standard
  • Adequate sampling
  • Estimate the clinical utility

34
Choosing the Threshold of a Test
Downs Syndrome
Normal Karyotype
Probability density function
NL Risk
Downs Risk
AFP
35
Complexity and other characteristics of tests can
also be quantified
36
Summary
  • Accuracy of a diagnostic test can be summarized
    by two measures sensitivity specificity
  • These numbers determine likelihood ratios, LR
  • LR can be used on the odds form of Bayes formula
    to design a study or evaluate the post-test odds
    of disease or on a 2 x 2 table
  • The threshold to call a test , is a trade-off
    between sensitivity specificity, costs, and
    treatment benefits/risks

37
Next Week
  • 330-545 in Wohl Auditorium
  • Scientific writing Jeanne Erdmann. Read
  • Writing your manuscript
  • "INSTEAD OF" and "TRY" tips in writing
  • Time-to-event analysis Brian Gage. Read
  • Katz MH. Multivariable Analysis A Primer for
    Readers of Medical Research Ann Intern Med 2003
  • Katz MH et al. Proportional hazards (Cox)
    regression. J Gen Intern Med 1993

38
More Examples 1 Lyme Disease
  • Antibody assay
  • Sensitivity 95 specificity 95
  • High Lyme Disease prevalence (20)
  • Positive predictive value 83
  • Low Lyme Disease prevalence (2)
  • Positive predictive value 28
  • Brown SL. JAMA 199928262-6.

39
More Examples 2 Use of D-Dimer post Joint
Replacement
  • Suppose youd like to use the D-Dimer test to
    determine how long to prescribe an anticoagulant
    after orthopedic surgery, a controversial area
  • Youd like to test the hypothesis that
    anticoagulant therapy can be stopped once the
    D-Dimer falls to a certain level.
  • Youre not sure what that level is because
    surgery can elevate the D-Dimer.
  • Theres also no agreement on how long to Rx an
    anticoagulant (3 to 42 days is used).
  • How would you design a study to answer these
    questions?

40
More Examples 3 Mammography
  • Mammography in women between 40-50 yrs
  • If 100,000 low-risk women are screened
  • 6,034 mammograms will have some abnormality
  • 5,998 (99.4) will be false-positive
  • 36 will actually have breast cancer
  • Why? Prevalence 0.036
  • Hamm RM. J Fam Pract 19984744-52.
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