Scales and Measurements

1. SENSITIVITY AND SPECIFICITY
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2. Measures how good a test is at detecting binary features
of interest
(disease / non disease)
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3. TERMS:
TRUE POSITIVE: Sick people correctly identified as sick
FALSE POSITIVE: healthy people incorrectly identified
as sick
TRUE NEGATIVE: Healthy people correctly identified as
healthy
FALSE NEGATIVE: sick people incorrectly identified as
healthy
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4. EXAMPLE
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Test
Results
Disease Status
Presence Absence Total
Positive True Positive False
Positive
Total
Positive
Negative False
Negative
True
Negative
Total
Negative
Total Total With
Disease
Total
Without
Disease
Total
DIAGNOSTIC ACCURACY

5. SENSITIVITY
If a person has a disease
How often will the test be positive (true positive)
High Sensitivity: HELPS TO RULE OUT DISEASE
Sensitivity = True Positive / True Positive + False Negative
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6. SENSITIVITY (POSITIVE IN DISEASE)
Sensitivity is the ability of a test to correctly classify an individual as
diseased
Sensitivity: = a / a+c
= a (true positive) / a+c(true positive + false negative)
Probability of being test positive when disease present
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7. SPECIFICITY (NEGATIVE IN HEALTH)
If a person does not have the disease , how often will the test be
negative(true negative)
The ability of a test to correctly classify an individual as disease free
is called the test specificity
Specificity = d / b+d
d(true negative) / b+d (true negative + false positive)
Probability of being test negative when disease absent
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8. EXAMPLE
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Assessing Effectiveness Of Test: Sensitivity And Specificity
Test results Disease status
+ -
+ a b
- c d

9. Disease
Present Absent
Risk
factor
Present True
positive
(a)
False
positive
(b)
Absent False
negative
(c)
True
negative
(d)
Sensitivity = a/a+c Specificity = d/b+d
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10. Disease
Present Absent
Risk
factor
Present 27 9
Absent 34 93
Sensitivity: a/a+c, 27/27+34 = 44%
Specificity: d/b+d, 93/3+93 = 96%
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11. POSITIVE PREDICTIVE VALUE
It is the percentage of patients with a positive test who actually
have the disease
PPV = a / a+b
a(true positive) / a+b ( true positive + false positive )
Probability (patient having disease when test is positive)
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12. NEGATIVE PREDICTIVE VALUE
It is the percentage of patients with a negative test who do
not have the disease.
NPV = d / c+d
d ( true negative ) / c+d ( false negative + true negative)
Probability (patient not having disease when test is
negative)
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13. True positive False positive
False negative True negative
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14. 27 (a) 3 (b)
34(c) 93 (d)
PV + = a/a+b = 27/27+3 = 90%
PV - = d/c+d = 93/34+93 = 73%
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15. LIKELIHOOD RATIO
POSITIVE LIKELIHOOD RATIO
Likelihood ration of positive test explains how much the
odds of disease increase when test is positive
LR + = Sensitivity
----------------------
1- Specificity
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16. LIKELIHOOD RATIO
POSITIVE LIKELIHOOD RATIO
LR + = Sensitivity
----------------------
1- Specificity
44/100-96 = 11
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17. LIKELIHOOD RATIO
NEGATIVE LIKELIHOOD RATIO
Likelihood ration for negative test explains how much
odds of disease decrease with negative test.
LR - = 1 - Sensitivity
-------------------------
Specificity
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18. LIKELIHOOD RATIO
NEGATIVE LIKELIHOOD RATIO
LR - = 1 - Sensitivity
-------------------------
Specificity
100-44 / 96 = 0.58
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19. LIKELIHOOD RATIO
Interpreting LR
LR Ranges from 0 to ∞
LR = 0, Test provides no additional information
LR >1, Greater then 1, Increase the likelihood of the
disease respectively
LR<1, Less than 1, Decrease the likelihood of the disease
respectively
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