Screening and Diagnostic Testing
Sue Lindsay, Ph.D., MSW, MPH
Division of Epidemiology and Biostatistics
Institute for Public Health
San Diego State University
Early Diagnosis of Disease
• Prompt attention to the earliest
symptoms
• Detection of disease in asymptomatic
individuals
Early Diagnosis of Disease
• Screening and diagnostic tests
improve the ability to estimate
the probability of the presence
or absence of a disease
Screening vs. Diagnostic Tests
Screening Tests
• Tests performed on asymptomatic individuals with the
goal of detecting pre-clinical cases of disease
Diagnostic Tests
• Tests performed to increase probability of disease
identification and confirmation in cases of suspected
disease
How good is your test?
The Progress of Disease
Disease or precursor detectable by screening
Disease
begins
pre-clinical
Symptoms
begin
Exposure
Death
Screening
Test +
lead time
Disease confirmed by diagnostic testing
“Gold standard”
Considerations for Screening Programs
1. The disease should be a significant public health problem
2. There should be a recognizable latent or early symptomatic
stage
3. There should be a suitable screening test acceptable to the
population
4. There should be well-established and available diagnostic tests
5. There should be an accepted treatment for the disease
6. Facilities for diagnosis and treatment should be available
7. The cost of case-finding, diagnosis, and treatment should be
anticipated
8. The process should be regular and on-going
Participation in Screening Programs
1. The disease must be known to the individual.
2. It must be regarded as a serious threat to health
3. Each individual must feel vulnerable to the disease
4. There must be a firm belief that action will have meaningful
results
The Screening 2X2 Table
Disease
No Disease
Test Positive
a
true-positives
b
false-positives
Test Negative
c
false-negatives
d
true-negatives
Prevalence of disease =
a+c
a+b+c+d
Sensitivity and Specificity
Disease
No Disease
Test Positive
a
true-positives
b
false-positives
Test Negative
c
false-negatives
d
true-negatives
Sensitivity =
a
Specificity =
a+c
True positives
=
All with disease
d
b+d
True negatives
=
All without disease
Important!
• Determination of the sensitivity and specificity of a test
requires that a diagnosis of disease be established or
ruled out for every person tested by the screening
procedure, regardless of whether he screens negative
or positive
• The diagnosis must be established by techniques
independent of the screening test
Sensitivity and Specificity are
descriptors of the accuracy of a test
Sensitivity
• The greater the sensitivity, the more likely the tests will detect persons with
the disease.
• A negative result on a test with excellent sensitivity can virtually rule out
disease
Specificity
• The greater the specificity, the more likely it is that persons without the
disease will be excluded
• A positive result on a test with excellent specificity will strongly suggest the
presence of disease.
Sensitivity and Specificity
Disease
No Disease
Test Positive
a
true-positives
b
false-positives
Test Negative
c
false-negatives
d
true-negatives
Sensitivity =
a
Specificity =
a+c
True positives
=
All with disease
d
b+d
True negatives
=
All without disease
Sensitivity and Specificity
Diabetes
No Diabetes
Glucose Tolerance
Positive
34
20
Glucose Tolerence
Negative
116
9,830
Sensitivity =
34
Specificity =
34 +116
= 22.6%
9,830
20 + 9,830
=
99.7%
Predictive Value
Disease
No Disease
Test Positive
a
true-positives
b
false-positives
Test Negative
c
false-negatives
d
true-negatives
Positive Predictive Value =
PV+
=
True positives
All who test positive
a
a+b
Negative Predictive Value =
PV-
=
d
c+d
True negatives
All who test negative
Predictive Values are estimates of the
probability of the presence or absence
of disease based on the test result
Positive Predictive Value
• The percentage of persons with positive test results who actually have the
disease
• How likely is it that the disease of interest is present if the test is positive?
Negative Predictive Value
• The percentage of persons with negative test results who do not have the
disease of interest
• How likely is it that the disease of interest is not present if the test is
negative?
Predictive Value
Disease
No Disease
Test Positive
a
true-positives
b
false-positives
Test Negative
c
false-negatives
d
true-negatives
Positive Predictive Value =
PV+
=
True positives
All who test positive
a
a+b
Negative Predictive Value =
PV-
=
expensive !
d
c+d
True negatives
All who test negative
Predictive Value
Intraocular pressure +
Intraocular pressure -
Positive Predictive Value =
PV+
Glaucoma
No glaucoma
140
80
10
910
140
140 + 80
= 64%
Negative Predictive Value =
PV-
= 99%
910
10+910
Screening and Diagnostic Tests
Breast Cancer
• Clinical Breast Exam
• Screening Mammogram
• Diagnostic Mammogram
• Fine Needle Aspiration Biopsy
• Core Biopsy
• Excisional Biopsy (gold standard)
Predictive Values are Influenced by
Prevalence of Disease
Disease
No disease
Test positive
36
48
Test positive
9
50
Test negative
4
912
Test negative
1
940
Disease
No disease
1,000
1,000
Prevalence = 40/1,000 = 4%
Sensitivity =
36/40 = 90%
Specificity = 912/960 = 95%
PV+ = 36/84
= 43%
PV- = 912/916 = 99.5%
Prevalence = 10/1,000 = 1%
Sensitivity = 9/10
= 90%
Specificity = 940/990 = 95%
PV+ = 9/59
= 15.3%
PV- = 940/941 = 99.8%
Yield
The yield of a screening test is the amount of previously
unrecognized disease that is diagnosed with screening
1. Yield is influenced by:
1. The sensitivity of the test
2. The prevalence of unrecognized disease in the population
2. In screening tests, a high positive predictive value is desirable.
3. However, if the prevalence of a disease is low, even a highly sensitive
test will yield a low positive predictive value
4. For the most yield, screening should be aimed at populations with a
high prevalence of disease
An Example
A manufacturer would like to sell you a new rapid
screening test developed to screen for strep throat.
You know the prevalence of strep throat in your
pediatric population in the high peak season is 27%.
The manufacturer of the new test describes the
sensitivity as 70% and the specificity as 73%.
Assuming that you will use this test with 1,000
children, what are the positive and negative
predictive values of this test in your population?
Would you buy this product?
Strep Throat Example
Strep Throat
No Strep Throat
189
197
386
81
533
614
270
730
1,000
Test positive
Test negative
Prevalence is 27%
Sensitivity is 70%
Positive predictive value = 189/386 = 49%
Specificity is 73%
Negative predictive value = 533/614 = 87%
Likelihood Ratios
Likelihood ratios do not vary with prevalence
The probability of a particular test result for a person with the disease
The probability of a particular test result for a person without the disease
Likelihood Ratios
Likelihood Ratio for a Positive Test
• The probability of a positive test result for a person with the disease
The probability of a positive test result for a person without the disease
• The larger the size of the LR+, the better the diagnostic value of the test
• An LR+ value of 10 or greater is considered a good test
Likelihood Ratio for a Negative Test
• The probability of a negative test result for a person with the disease
The probability of a negative test result for a person without the disease
• The smaller the size of the LR-, the better diagnostic value of the test
• An LR- value of 0.10 or less is considered a good test
Likelihood Ratio
Disease
No Disease
Test Positive
a
true-positives
b
false-positives
Test Negative
c
false-negatives
d
true-negatives
Likelihood ratio for positive test =
LR+ =
a/a+c
b/b+d
Likelihood ratio for neg test =
Sensitivity
(1-Specificity)
LR- =
(1-Sensitivity)
Specificity
c/a+c
d/b+d
Likelihood Ratio is Not Influenced by
Prevalence
Test positive
Test negative
Disease
No disease
36
48
Test positive
9
50
4
912
Test negative
1
940
Disease
No disease
1,000
1,000
Prevalence = 10/1,000 = 1%
Sensitivity = 9/10
= 90%
Specificity = 940/990 = 95%
Prevalence = 40/1,000 = 4%
Sensitivity = 36/40 = 90%
Specificity = 912/960 = 95%
LR+ = 36/40
48/960
=
0.90 =
0.05
18
LR+ = 9/10
50/990
LR- = 4/40
912/960
=
0.10 =
0.95
0.10
LR- =
=
1/10
=
940/990
0.90 =
0.05
18
0.10 = 0.10
0.95
Cut-Points for Screening Tests
Screening Tests with Categorical Results:
• Mammography:
•
•
•
•
•
BIRADS
BIRADS
BIRADS
BIRADS
BIRADS
1:
2:
3:
4:
5:
negative
benign
probably benign
suspicious for cancer
highly suggestive for malignancy
• What is Abnormal?
• The decision about what results to call “abnormal” will effect sensitivity,
specificity, and predictive values of your screening tests.
Cut-Points for Screening Tests
Screening Tests with Continuous Results:
• Blood Pressure
• Cholesterol Levels
• Blood sugar
• What is Abnormal?
• There are many options concerning where to set the cut-off point
• Along a continuous scale, different cut-off points will result in differing levels of
sensitivity and specificity
• As sensitivity increases, specificity decreases
• Low cut-points are very sensitive, but not specific
• Those with disease are correctly classified, but those without disease are not
• High cut-points are very specific, but not sensitive
• Those without disease are correctly classified, but those with disease are not
• How to you decide the cut-off point?
Blood Glucose and Diabetes
Sensitivity and Specificity at Different Cut-Off Points
Blood Glucose Level
Sensitivity
Percent diabetics
correctly identified
200
180
160
140
120
100
80
37
50
56
74
89
97
100
Specificity
Percent non-diabetics
correctly identified
100
99
98
91
68
25
2
ROC Curves
(Receiver Operating Characteristics)
Sensitivity
(signal)
(1-Specificity)
(noise)
The Evaluation of Screening Programs
Does early detection of disease:
1. Reduce morbidity?
2. Reduce mortality?
3. Improve quality of life?
4. Reduce cost of disease?
Bias in the Evaluation of Screening Programs
Lead-Time Bias
• Survival time is increased in those screened because of earlier detection
• May be no actual improvement in disease progression or mortality
Length-Biased Sampling
• Disease detected by screening is less aggressive than disease detected
without screening. Cases detected with a screening program tend to have
longer pre-clinical stages than those missed by screening
Patient Self-Selection Bias
• Individuals who participate in screening programs may differ from those
who do not on characteristics that may be related to survival