Download lect4(100103)

Lecture 4:
Assessing Diagnostic and
Screening Tests
Reading:
Gordis - Chapter 4
Lilienfeld and Stolley - Chapter 6, pp. 117-125
Screening
• “Screening is the application of a test to people
who are asymptomatic for the purpose of
classifying a person with respect to their
likelihood of having a particular disease”
• Screening, in and of itself, does not diagnose
disease.
– Persons who test positive are referred to
physicians for more detailed assessment
– Physicians determine the presence or
absence of disease.
• Screening is one of the most practical
applications of epidemiology. It’s goal is to
promote health and prevent disease.
Perform
screening
test
Negative
result
Record
the
result
Inform the
person
screened
Record
the
result
Inform
the
patient
Positive result
Perform
diagnostic
test
Negative
result
Positive result
Start
treatment
Negative
response
Positive response
Continue
treatment
and
reevaluate
Revise
treatment
and
reevaluate
Screening
Decision
Tree
Outcomes in a screening test
• False positive – when a screening test
indicates that the individual has a disease but
the person in fact does not have the disease.
• False negative – when a screening test
indicates that the individual does not have a
disease but the person in fact has the
disease.
• True positive – when the test says the person
has a disease and the person indeed has the
disease.
• True negative – when the test says the
person does not have the disease and the
person in fact is disease free.
Screening tests
• Validity of test is shown by how well the test
actually measures what it is supposed to
measure. Validity is determined by the
sensitivity and specificity of the test.
• Reliability is based on how well the test does
in use over time - in its repeatability.
Sensitivity and specificity:
tests of validity
• Sensitivity is the ability of a screening
procedure to correctly identify those who
have the disease--the percentage of those
who have the disease and are proven to have
the disease as demonstrated by a diagnostic
test.
• Specificity is the ability of a screening
procedure to correctly identify the percentage
of those who do not have the disease--those
who do not have the disease and are proven
to not have the disease as demonstrated by a
diagnostic test.
Screening
Diagnosed disease status
Positive
Negative
Total
Screening test
Positive a=true positive
Negative c=false negative
Total
a+c
Sensitivity =
a
b+c
b=false positive
d=true negative
a+b
c+d
b+d
Specificity =
d
b+d
Sensitivity and specificity of breast
cancer screening examination
Breast Cancer
Cancer
confirmed
Cancer not
confirmed
Total
Positive
132
983
1155
Negative
45
63,650
63,695
177
64,633
64,820
Screening test
Total
Sensitivity =
132/177 = 74.6%
Specificity =
63650/64633 = 98.5%
Screening
• Positive predictive value – Probability that a
person actually has the disease given a
positive screening test
• Negative predictive value – Probability that
a person is actually disease-free given a
negative screening test
Screening
Diagnosed disease status
Positive
Negative
Total
Screening test
Total
Positive
a
b
a+b
Negative
c
d
c+d
a+c
b+d
Positive predictive value =
a
a+b
Negative predictive value =
d
c+d
Prevalence on positive predictive value
with constant sensitivity and specificity
Prevalence
(%)
0.1
1.0
5.0
50.0
PV+
(%)
1.8
15.4
48.6
94.7
Sensitivity
(%)
90
90
90
90
Specificity
(%)
95
95
95
95
• The higher the prevalence, the higher the
predictive value.
• Screening is most productive if it is applied to a
high-risk population.
Cutoff level and validity
• When the test is a continuous variable,
we need a cutoff level to decide positive
or negative test result.
• If increase the sensitivity by lowering
the the cutoff level, we decrease the
specificity.
Choice of cutoff
• The choice of cutoff level depends on the
importance attached to false positives and
false negatives.
• False positives associated with costs –
emotional and financial;
false negative associated with missing early
detection.
How do we examine the
reliability (repeatability)?
We do the tests repeatedly in the same
individuals and calculate measures of :
• Intrasubject variation (variation within
individual subjects)
• Interobserver variation (variation between
those reading the test results)
• Overall percent agreement
• Kappa statistic
Overall percent agreement
Reading No. 2
Reading No. 2
Abnormal Suspect Doubtful Normal
Abnormal
a
b
c
d
Suspect
e
f
g
h
Doubtful
i
J
k
l
Normal
m
n
o
p
Percent agreement =
a+f+k+p
Total reading
Kappa statistics
• We would expect agreement purely by chance.
• We want to know:
To what extent do readers agree beyond what we
would expect by chance alone?
• Answer: calculate Kappa statistics
• Kappa =
Observed agreement (%) - agreement expected by chance alone (%)
100% - agreement expected by chance alone (%)
Calculate Kappa statistics
Observed table
Expected table
Observer 1
+
Observer 1
-
+
-
Observer
2
Observer
2
+
16
2
18
+
12.8
5.2
18
-
16
11
27
-
19.2
7.8
27
32
13
45
32
13
45
Observed agreement =
(16+11)/45 = 60%
12.8 = 45x(18/45)x(32/45)
7.8 = 45x(27/45)x(13/45)
Expected agreement =
(12.8+7.8)/45 = 45.8%
Kappa = (60% - 45.8%) / (100%-45.8%) = 0.26
Interpreting the values of Kappa
Value of Kappa
Strength of agreement
0.0
No agreement
<0.2
Poor
0.21-0.4
Fair
0.41-0.6
Moderate
0.61-0.8
Good
0.81-1.00
Very good
Validity vs reliability
Test results
Reliable but invalid
Valid but not reliable
Both valid and reliable
True value