Explain to me the concept of likelihood ratio in screening
The likelihood ratio (LR) tells you how much a test result changes the probability of disease. A positive LR >10 or negative LR <0.1 is generally considered strong; values near 1.0 change nothing. They are calculated from sensitivity and specificity and are applied via Bayes' theorem (pre-test odds × LR = post-test odds).
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Likelihood Ratio in Screening and Diagnostic Testing
What Is a Likelihood Ratio?
A likelihood ratio (LR) is the probability that a given test result would occur in a patient with the disease, divided by the probability that the same result would occur in a patient without the disease. It tells you how much a test result should shift your suspicion of disease - either up (positive result) or down (negative result).
As Harrison's Principles of Internal Medicine 22E puts it, the LR "provides a measure of how well the test distinguishes those with from those without disease."
Why Use LR Instead of Sensitivity/Specificity Alone?
Sensitivity and specificity describe a test's accuracy, but they do not directly tell you how much a result will shift your clinical probability. That shift depends on the interaction between sensitivity, specificity, and the pretest probability - the LR captures all of this in a single number.
- Predictive values (PPV, NPV) also depend on disease prevalence, making them population-specific and non-transferable.
- LRs are independent of prevalence and can be applied to any individual patient given their specific pretest probability.
Two Types of Likelihood Ratio
1. Positive Likelihood Ratio (LR+)
"How much more likely is a positive result in someone WITH the disease vs. someone WITHOUT it?"
$$LR+ = \frac{\text{Sensitivity}}{1 - \text{Specificity}} = \frac{\text{True Positive Rate}}{\text{False Positive Rate}}$$
- A test with 90% sensitivity and 90% specificity gives LR+ = 0.90 / (1 - 0.90) = 9. A positive result is 9 times more likely in a diseased patient than a healthy one.
- Most tests in medicine have LR+ between 1.5 and 20.
2. Negative Likelihood Ratio (LR-)
"How much more likely is a negative result in someone WITH the disease (i.e., a false-negative) vs. someone WITHOUT it (a true-negative)?"
$$LR- = \frac{1 - \text{Sensitivity}}{\text{Specificity}} = \frac{\text{False Negative Rate}}{\text{True Negative Rate}}$$
- For the same 90%/90% test: LR- = (1 - 0.90) / 0.90 = 0.11. A negative result is only about 1/10th as likely in a diseased patient as in a healthy one.
Interpreting LR Values
| LR+ | Meaning |
|---|---|
| >10 | Large shift upward - excellent for ruling IN disease |
| 5-10 | Moderate shift upward - good test |
| 2-5 | Small shift - modest value |
| 1-2 | Minimal or no shift - poor test |
| = 1 | Useless - does not change probability at all |
| LR- | Meaning |
|---|---|
| <0.1 | Large shift downward - excellent for ruling OUT disease |
| 0.1-0.2 | Good test for ruling out |
| 0.2-0.5 | Moderate shift downward |
| >0.5 | Minimal shift - poor for ruling out |
The Textbook of Clinical Echocardiography gives a useful example: LV thrombus on echo with 95% sensitivity and 88% specificity yields LR+ = 7.9 (good) and LR- = 0.06 (excellent). The negative LR is excellent because a high-quality scan will almost never miss an apical thrombus.
The SnNout mnemonic: a high-Sensitivity test with a negative result rules out disease (low LR-).
The SpPin mnemonic: a high-Specificity test with a positive result rules in disease (high LR+).
Applying LR: Converting Pre-test to Post-test Probability
This is done via Bayes' theorem, most practically using the odds form:
$$\text{Post-test odds} = \text{Pre-test odds} \times LR$$
Then convert back: Probability = Odds / (1 + Odds).
Step-by-step example (from Harrison's):
- 41-year-old man, atypical chest pain, pretest probability of CAD = 10% (pre-test odds = 0.10/0.90 = 0.111)
- Exercise treadmill test is positive: LR+ = 2.4
- Post-test odds = 0.111 × 2.4 = 0.267
- Post-test probability = 0.267 / 1.267 = ~21% (modest change - the test didn't help much)
For a 60-year-old man with typical angina, pretest probability = 80%:
- Pre-test odds = 0.80/0.20 = 4.0
- Post-test odds = 4.0 × 2.4 = 9.6
- Post-test probability = 9.6 / 10.6 = ~91% (again, didn't change much because the pre-test probability was already high)
This illustrates a key principle: tests are most informative when pretest probability is intermediate (roughly 20-80%). At the extremes, even a good test barely moves the needle.
The LR Nomogram
Rather than doing the arithmetic each time, you can use the Fagan nomogram - a three-column graphical tool with pre-test probability on the left, LR in the middle, and post-test probability on the right. Place a ruler from the pre-test probability through the LR and read off the post-test probability directly.
Fagan nomogram - connect pretest probability to the likelihood ratio with a straightedge to read off posttest probability.
LR Table: How Much Does It Shift Probability?
From Symptom to Diagnosis, 4th Ed (Table 1-5):
| LR | Pre-test 5% | Pre-test 10% | Pre-test 20% | Pre-test 50% | Pre-test 70% |
|---|---|---|---|---|---|
| 10 | 34% | 53% | 71% | 91% | 96% |
| 3 | 14% | 25% | 43% | 75% | 88% |
| 1 | 5% | 10% | 20% | 50% | 70% |
| 0.3 | 1.5% | 3.2% | 7% | 23% | 41% |
| 0.1 | 0.5% | 1% | 2.5% | 9% | 19% |
Note how an LR of 1 changes nothing - the posttest probability equals the pretest. And an LR+ of 10 applied to a 50% pretest probability shoots you up to 91%.
Important Cautions
From Symptom to Diagnosis, 4th Ed:
- If pretest probability is HIGH, a negative test rarely rules out disease no matter how small the LR- is. You need another test.
- If pretest probability is LOW, a positive test rarely rules in disease no matter how large the LR+ is. This is why mass screening in low-prevalence populations generates many false positives - even an excellent test (high LR+) applied to a 1% prevalence population still gives a low post-test probability.
- LR = 1 is completely useless.
Summary
| Concept | Formula | Clinical Meaning |
|---|---|---|
| LR+ | Sensitivity / (1 - Specificity) | How much a positive result raises disease probability |
| LR- | (1 - Sensitivity) / Specificity | How much a negative result lowers disease probability |
| Post-test odds | Pre-test odds × LR | Bayes' theorem in odds form |
| LR+ >10 | Rules IN (SpPin) | High specificity drives this |
| LR- <0.1 | Rules OUT (SnNout) | High sensitivity drives this |
| LR = 1 | Useless test | No shift in probability |
Sources: Harrison's Principles of Internal Medicine 22E, pp. 67-68 | Textbook of Clinical Echocardiography, p. 147 | Symptom to Diagnosis, 4th Ed, pp. 20-21
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