Screening for Disease. An Ounce of Prevention is Worth a Pound of Cure. An Ounce of Prevention is Better Than a Pound of Cure. Actually,…. If prevention hasn ’ t been effective, perhaps early identification of disease by a screening test would be beneficial. Disease detectable
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An Ounce of Prevention is Worth a Pound of Cure.
An Ounce of Prevention is Better Than a Pound of Cure.
Actually,…
If prevention hasn’t been effective,
perhaps early identification of disease
by a screening test would be beneficial.
detectable
by screen
DPCP
(Detectable
preclinical
phase)
Death with
screening &
early treatment
A Hypothetical Time Line for Disease
Death
without
screening
& early Rx
Biologic
onset of
disease
Symptoms
develop
Birth
With screening, early diagnosis and treatment may result in longer survival, less disability, decreased recurrence, etc.
You can think about these as screening tests that hopefully enable you to identify disease at an early stage:
Self breast exam
Clinical Breast exam
Mammography
Digital rectal exam
PSA
Dip stick test for sugar
“Routine” blood tests
Routine EKG or “stress test”
Skin test for TB
Characteristics of a good screening test?
Characteristics of a Good Screening Test enable you to identify disease at an early stage:
Sources of Variability (Error) enable you to identify disease at an early stage:
BP = 165/95
Observation (Test) Variability:
Other Sources of Variability enable you to identify disease at an early stage:
Biological (Subject) Variability:
… how good is the PSA test with respect to determining whether he has prostate cancer or not? (Test validity)
You have prostate cancer. enable you to identify disease at an early stage:
PSA=10
Is it possible he has been misclassified?
What do we do if PSA=5.6? enable you to identify disease at an early stage:
Measurement
NCI Risk Classification
Does the test accurately distinguish healthy & diseased people?
The ideal is to have a test that is exquisitely sensitive & specific….
30
Test 
Test +
20
Men without cancer
# with a given test result
10
Men with
cancer
0
1 5 10 15
PSA (Prostatespecific Antigen) Values
The people?reality is that test values from diseased and nondiseased people often overlap….
No cancer
30
# of men
with a given
test result
20
Men with
prostate cancer
10
0
1 5 10 15
PSA (Prostatespecific Antigen) Values
What if >5 is deemed abnormal? Is the test valid?
… but maybe not as much overlap as in the previous example.
No cancer
30
# of men
with a given
test result
20
Men with
prostate cancer
10
0
1 5 10 15
PSA (prostate specific antigen)
How can we think about test validity in a structured way?
Men with a Variety of PSA Test Results example.
1
1
1
1
1
1
1
1
1
These have low probability of having cancer.
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
4
4
4
4
4
4
4
4
4
10
10
These have a higher probability of having cancer.
10
10
10
10
10
10
9 example.
9
10
10
8
8
6
10
10
10
10
10
10
10
10
9
9
1
1
2
2
1
1
1
1
1
1
1
1
1
1
1
1
2
1
1
2
1
1
2
4
2
4
6
1
4
4
1
2
2
4
1
1
4
4
1
1
2
2
4
4
4
1
1
2
1
2
2
1
1
4
4
2
1
4
1
1
2
4
2
.
Of those who were diseased, what was the probability (%) of correctly being identified by a + screening test?
Of those who were NOT diseased, what was the probability (%) of correctly having a () screening test?
If you screened (+), what was the probability that you had cancer?
Test +
If you screened (), what was the probability that you did NOT have cancer?
Test 
Measures of Test Validity example.
Does the test accurately distinguish between healthy and diseased people?
Two Perspectives
{
Sensitivity: probability that diseased people test +
Specificity: probability that nondiseased people test 
Probability
of correct test
(screening)
{
Predictive value (+): if someone has a + test, what is the probability that they actuallyhavethe disease?
Predictive value (): if someone has a  test, what is the probability that they don’t have the disease?
Probability
of disease
A Different Type of 2x2 Table example.
True disease status
Diseased
Not Diseased
Positive
Negative
a
b
Test Results
c
d
Sensitivity example.
Among those who really had disease, the probability that the test correctly identified .
Not Diseased
Diseased
Test Positive
Test Negative
True + ?
a
b
c
d
Total diseased
= a+c
a
a+c
Sensitivity=
Sensitivity example.
Among those who really had disease, the probability that the test correctly identified them as positive.
Not Diseased
Diseased
Test Positive
Test Negative
132
983
True Positive
a
b
45
63,650
c
d
177 64,633
132
177
Sensitivity =
=74.6%
Specificity example.
d
b+d
Among those who really did NOT have disease, the probability that the test correctly identified them as ().
Not Diseased
Diseased
Test Positive
Test Negative
132
983
True Positive
a
b
True Negative
45
63,650
c
d
177 64,633
63,650
64,633
=
Specificity =
=98.5%
Very specific example.
Very sensitive
The TradeOff: Sensitivity Versus Specificity
The ideal is to have a test that is exquisitely sensitive & highly specific, but this frequently isn’t the case.
No cancer
30
20
# with a given test result
Men with
cancer
10
0
1 5 10 15
If PSA values for men with prostate cancer overlap those of men without cancer, what do you use as the criterion for “abnormal”?
We example.’ve been looking at the performance of the test (its validity) by asking:
Among people who truly have the disease, what is the probability that the test will identify it them as diseased?
and
Among people who don’t have the disease, what is the probability that the test will categorize them as nondiseased?
If I have a test done and it is positive (abnormal), what is the probability that I really have the disease?
If I have a test done and it is negative (normal), what is the probability that I really don’t have the disease?
2) If the test was example.negative,
how likely is it that he really does NOT have it?
[How reassured should he be?]

When thinking about
Predictive Value of a Test, ...
… imagine you are a physician discussing
the results of a screening test with a patient.
+
Positive example.
Predictive Value
Among men with positive test results, what is the probability that they have disease?
Diseased
Not Diseased
Test Positive
132 983 1,115
45 63,650 63,695
177 64,633 64,810
a
b
Test Negative
c
d
Predictive value (+) = a = 132 = 11.8%
a+b 1,115
Negative example.
Predictive Value
Among men with negative test results, what is the probability that they DON’T have disease?
Diseased
Not Diseased
132 983 1,115
45 63,650 63,695
177 64,633 64,810
Test Positive
a
b
Test Negative
c
d
Predictive value () = d = 63,650 = 99.9%
c+d 63,695
What is the prevalence of HIV in this population? example.
True Disease Status
HIV+ HIV 
10 510 520
0 99,480 99,480
10 99,990 100,000
Positive
Negative
Test
Prevalence = 10/100,000 = 0.0001= 0.01%
Sensitivity=100% example.
Specificity=99.5%
True Disease Status
HIV+ HIV 
10 510 520
0 99,480 99,480
10 99,990 100,000
Positive
Negative
Test
Prevalence in female
blood donors=0.01%
Predictive value (+) = a = 10 = 1.9%
a+b 520
Sensitivity=100% example.
Specificity=99.5%
True Disease Status
HIV+ HIV 
4,000 480 4,480
0 95,520 95,520
4,000 96,000 100,000
Positive
Negative
Test
Prevalence in males
at an STD clinic=4%
Predictive value (+) = a = 4,000 = 83%
a+b 4,480
NOTE: Predictive value is example.GREATLY influenced by the prevalence of disease in the population being screened.
Sensitivity=100%
Specificity=99.5%
True Disease Status
HIV+ HIV 
20,000 400 20,400
0 79,600 79,600
20,000 80,000 100,000
Positive
Negative
Test
Prevalence in IV drug users=20%
Predictive value (+) = a = 20,000 = 98%
a+b 20,400
True Positives example.
False Positives
False Negatives
True Negatives
What About Cells “b” and “c” ???
True disease status
Diseased Not Diseased
132 983 1,115
45 63,650 63,695
177 64,633 64,810
Positive
Negative
b
a
Test
d
c
Hazards of Screening example.
True disease status
Diseased Not Diseased
132 983 1,115
45 63,650 63,695
177 64,633 64,810
Positive
Negative
False Positives: worry, cost, complications, risk, of diagnostic tests
Test
False Negatives: false reassurance
What Should We Screen For? example.
What Diseases are Appropriate for Screening? example.
High blood pressure leads to kidney damage, atherosclerosis, & stroke. It has a long DPCP and can be effectively treated with diet and medication. Undiagnosed HBP is very common.
How Do We Assess Screening Programs? example.(Feasibility and Effectiveness)
Evaluating a Screening Program example.
SelfSelection Bias (Volunteer Bias) example.
Lead time is a good thing!
But, it exaggerates the benefit of screening.
Survival time with screening
DPCP
Death with
screening
Biologic
onset of
disease
Disease
detectable
by screen
Survival time without screening
DPCP
Death without
screening
Survival time with screening
DPCP
Death with
screening
Biologic
onset of
disease
Actual increase
in survival
Disease
detectable
by screen
Survival time without screening
DPCP
Death without
screening
Length example. Time Bias
Note that the length of the DPCP varies from person to person.
Some prostate cancers
are biologically aggressive and have short DPCPs.
What does a short DPCP mean?
… others are slower growing
and have longer DPCPs.
Length example. Time Bias
Screening has a better chance of detecting those with a long detectable preclinical phase, e.g. less aggressive tumors with more favorable prognosis.
Screening
No Screening
Mean DPCP of unscreened: 6 yrs
Mean survival: 4 yrs
Mean DPCP of screen +: 8 yrs
Mean survival of screen +: 6 yrs
Evaluating a Screening Program example.