This document discusses predictive value, likelihood ratios, and how to calculate and apply them. Predictive value reflects a test's diagnostic power and depends on sensitivity, specificity, and disease prevalence. The positive predictive value is the probability a positive test truly has the disease, while the negative predictive value is the probability a negative test truly doesn't have the disease. Likelihood ratios compare true positives and negatives to false ones to determine if a test result changes the probability a disease is present.

1. PREDICTIVE VALUE AND
LIKELIHOOD RATIO

2. PREDICTIVE VALUE
• Predictive value of a positive test result or
positive predictive value(PPV)
PPV = True positive/(true positive + false positive)
• Predictive value of a negative test result or
negative predictive value(NPV)
NPV = True negative/(true negative + false negative)

3. Disease Condition
(as determined by "Gold standard")
Positive Negative
False Positive → Positive
Positive True Positive
predictive value
Test
outcome
False Negative → Negative
Negative True Negative
predictive value
↓ ↓
Sensitivity Specificity

4. • Predictive value reflects the diagnostic power of a
test.
• Depends upon sensitivity, specificity and disease
prevalence.
• A high +PV indicates a strong chance that a person
with a positive test has the disease.
• A high -PV means that a negative test in effect rules
out the disease.

5. LIKELIHOOD RATIO(LR)
• Used for assessing the value of performing a
diagnostic test.
• Uses the sensitivity and specificity of the test.
• To determine whether a test result usefully
changes the probability that a disease state
exists.

6. CALCULATION
• Two versions : one for +ve test results(LR+)
and other for –ve test results(LR-).

7. APPLICATION
• LR >1 - test result is associated with the
disease.
• LR <1 - test result is associated with the
absence of the disease.
• LR =1 - little practical significance.
• LR+ >5 or LR- <0.2 – can be applied to the
pretest probability to estimate a post test
probability of the d/s state existing.

8. • Pretest odds of a particular diagnosis
multiplied by the likelihood ratio determines
the post test odds.

9. Patients with bowel cancer
(as confirmed on endoscopy)
Positive Negative
→ Positive predictive
value
= TP / (TP + FP)
Positive TP = 2 FP = 18
= 2 / (2 + 18)
= 2 / 20
FOB = 10%
test → Negative predictive
value
= TN / (FN + TN)
Negative FN = 1 TN = 182
= 182 / (1 + 182)
= 182 / 183
≈ 99.5%
↓
↓
Specificity
Sensitivity
= TN / (FP + TN)
= TP / (TP + FN)
= 182 / (18 +
= 2 / (2 + 1)
182)
=2/3
= 182 / 200
≈ 66.67%
= 91%

10. • LR+ = sensitivity/(1-specificity)
= 66.67%/(1-91%)= 7.4
• LR- = (1-66.67%)/91%= 0.37