5. Suppose a test for a disease is 85% accurate for those who have the disease (true positives) and 85% accurate for those who do not have the disease (true negatives). Within a sample of 4000 patients, the incidence rate of the disease is the national average. Complete parts (a) through (c) below. Disease No Disease Total Tested positive 51 591 642 Tested negative 9. 3349 3358 Total 60 3940 4000 a. Of those with the disease, what percentage test positive? b. Of those who test positive, what percentage have the disease? C. Compare the results in parts (a) and (b) and explain why they are different.

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#5 a, b, and c.
### Understanding Disease Test Accuracy

#### Problem Statement

Suppose a test for a disease is 85% accurate for those who have the disease (true positives) and 85% accurate for those who do not have the disease (true negatives). Within a sample of 4000 patients, the incidence rate of the disease is the national average. Complete parts (a) through (c) below.

#### Data Table:

|                       | Tested Positive | Tested Negative | Total |
|-----------------------|-----------------|-----------------|-------|
| **Disease**           | 51              | 9               | 60    |
| **No Disease**        | 591             | 3349            | 3940  |
| **Total**             | 642             | 3358            | 4000  |

### Questions:

a. **Of those with the disease, what percentage test positive?**

To find the percentage of people with the disease who test positive:
\[ \text{Percentage} = \left( \frac{\text{Number who tested positive and have the disease}}{\text{Total with the disease}} \right) \times 100 \]
So,
\[ \text{Percentage} = \left( \frac{51}{60} \right) \times 100 \approx 85\% \]

b. **Of those who test positive, what percentage have the disease?**

To find the percentage of people who test positive and actually have the disease:
\[ \text{Percentage} = \left( \frac{\text{Number who tested positive and have the disease}}{\text{Total who tested positive}} \right) \times 100 \]
So,
\[ \text{Percentage} = \left( \frac{51}{642} \right) \times 100 \approx 7.95\% \]

c. **Compare the results in parts (a) and (b) and explain why they are different.**

- **Part (a)** represents the sensitivity of the test, which is the probability that the test correctly identifies someone with the disease as positive. This is high at 85%.
  
- **Part (b)** represents the positive predictive value of the test, which is the probability that someone who tests positive actually has the disease. This is much lower at approximately 7.95%.

This discrepancy can be explained by the incidence rate of the disease being relatively low. Even though the test is highly
Transcribed Image Text:### Understanding Disease Test Accuracy #### Problem Statement Suppose a test for a disease is 85% accurate for those who have the disease (true positives) and 85% accurate for those who do not have the disease (true negatives). Within a sample of 4000 patients, the incidence rate of the disease is the national average. Complete parts (a) through (c) below. #### Data Table: | | Tested Positive | Tested Negative | Total | |-----------------------|-----------------|-----------------|-------| | **Disease** | 51 | 9 | 60 | | **No Disease** | 591 | 3349 | 3940 | | **Total** | 642 | 3358 | 4000 | ### Questions: a. **Of those with the disease, what percentage test positive?** To find the percentage of people with the disease who test positive: \[ \text{Percentage} = \left( \frac{\text{Number who tested positive and have the disease}}{\text{Total with the disease}} \right) \times 100 \] So, \[ \text{Percentage} = \left( \frac{51}{60} \right) \times 100 \approx 85\% \] b. **Of those who test positive, what percentage have the disease?** To find the percentage of people who test positive and actually have the disease: \[ \text{Percentage} = \left( \frac{\text{Number who tested positive and have the disease}}{\text{Total who tested positive}} \right) \times 100 \] So, \[ \text{Percentage} = \left( \frac{51}{642} \right) \times 100 \approx 7.95\% \] c. **Compare the results in parts (a) and (b) and explain why they are different.** - **Part (a)** represents the sensitivity of the test, which is the probability that the test correctly identifies someone with the disease as positive. This is high at 85%. - **Part (b)** represents the positive predictive value of the test, which is the probability that someone who tests positive actually has the disease. This is much lower at approximately 7.95%. This discrepancy can be explained by the incidence rate of the disease being relatively low. Even though the test is highly
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