Sensitivity, Specificity, Positive Predictive Value, & Negative Predictive Value Please review this Excel spreadsheet: Week 2 Epidemiological Examples (please see at

Sensitivity, Specificity, Positive Predictive Value, & Negative Predictive Value

Please review this Excel spreadsheet: Week 2 Epidemiological Examples (please see attached document)

In your post, you are to select a disease that no one else has selected. You can post early in the week to reserve your disease and then just reply to your original post when your material is done. Answer the following questions with your initial post.

1. What disease did you select? (Liver and pancreatic cancer

2. Find two different incidence rates for this disease. This can be either the rates of the disease in two different countries, age groups, ethnicities, occupation, or any other classifier that might be important to your disease. Provide the two different rates here. Cite your source.

3. Select a screening test or a confirmatory test for your disease. Report on what the sensitivity and specificity percentages are and cite your source. Also, provide the cost of the test.

4. Using the method outlined in the excel document determine the PPV and NPV of using both screening tests in the two populations you selected.

5. How much did each true positive cost?

6. Does it make sense to implement mandatory screening in either population? Why or why not?

Sen & Spec

Example 1
Disease (+) Disease (-)
Test (+) a (True Positive) b (False Positive) All Test Positive
Test (-) c (False Neg) d (True Negative) All Test Negative
All Diseased All Well Total Pop
Sensitivity 90% a/a+c
Specificity 95% d/d+b
Fake Data on an UNKNOWN DISEASE AND TEST
Disease (+) Disease (-)
Test (+) 475 4974 5449
Test (-) 53 94499 94551
528 99472 100000

What you see below is a 2 x 2 table. We will be using it to explain how to calculate sensitivity and specificity. Once that is explained, we will move on how to use sensitivity and specificity data along with incidence information to estamate how many people will be found using a screening program.

As you can see from our fictitious example, the fake screening test that we are talking about using would give 53 people a negative result when they were sick, 4,974 a positive result when they were not sick. This doesn’t sound like a great test, but that is all dependent on the natural history of the disease, mortality associated with it, and the communibility.

Please continue to the next sheet labeled PPV & NPV.

PPV & NPV

Example 1
Disease (+) Disease (-)
Test (+) Sensitivity (a) 1 – Specificity (b) All Test Positive
Test (-) 1 – Sensitivity ( c ) Specificity (d) All Test Negative
Incidence Number Population -Incidence Number Total Pop
PPV = A/A+B
NPV = D/D+C
Step 1: Insert Sensitivity and Specificity
Disease (+) Disease (-)
Test (+) 90% 1 – Specificity (b) All Test Positive
Test (-) 1 – Sensitivity ( c ) 95% All Test Negative
Incidence Number Population -Incidence Number Total Pop
Step 2: Calculate C & D
Disease (+) Disease (-)
Test (+) 90% 5% All Test Positive
Test (-) 10% 95% All Test Negative
Incidence Number Popul



Sensitivity, Specificity, Positive Predictive Value, & Negative Predictive Value
Please review this Excel spreadsheet: Week 2 Epidemiological Examples (please see attached document)
In your post, you are to select a disease that no one else has selected. You can post early in the week to reserve your disease and then just reply to your original post when your material is done. Answer the following questions with your initial post.
1. What disease did you select? (Liver and pancreatic cancer
2. Find two different incidence rates for this disease. This can be either the rates of the disease in two different countries, age groups, ethnicities, occupation, or any other classifier that might be important to your disease. Provide the two different rates here. Cite your source.
3. Select a screening test or a confirmatory test for your disease. Report on what the sensitivity and specificity percentages are and cite your source. Also, provide the cost of the test.
4. Using the method outlined in the excel document determine the PPV and NPV of using both screening tests in the two populations you selected.
5. How much did each true positive cost?
6. Does it make sense to implement mandatory screening in either population? Why or why not?





Sen & Spec



Example 1




Disease (+)
Disease (-)


Test (+)
a (True Positive)
b (False Positive)
All Test Positive


Test (-)
c (False Neg)
d (True Negative)
All Test Negative




All Diseased
All Well
Total Pop


Sensitivity
90%
a/a+c


Specificity
95%
d/d+b


Fake Data on an UNKNOWN DISEASE AND TEST




Disease (+)
Disease (-)


Test (+)
475
4974
5449


Test (-)
53
94499
94551




528
99472
100000



What you see below is a 2 x 2 table. We will be using it to explain how to calculate sensitivity and specificity. Once that is explained, we will move on how to use sensitivity and specificity data along with incidence information to estamate how many people will be found using a screening program.
As you can see from our fictitious example, the fake screening test that we are talking about using would give 53 people a negative result when they were sick, 4,974 a positive result when they were not sick. This doesn’t sound like a great test, but that is all dependent on the natural history of the disease, mortality associated with it, and the communibility.
Please continue to the next sheet labeled PPV & NPV.


PPV & NPV



Example 1




Disease (+)
Disease (-)


Test (+)
Sensitivity (a)
1 – Specificity (b)
All Test Positive


Test (-)
1 – Sensitivity ( c )
Specificity (d)
All Test Negative




Incidence Number
Population -Incidence Number
Total Pop






PPV = A/A+B






NPV = D/D+C


Step 1: Insert Sensitivity and Specificity




Disease (+)
Disease (-)


Test (+)
90%
1 – Specificity (b)
All Test Positive


Test (-)
1 – Sensitivity ( c )
95%
All Test Negative




Incidence Number
Population -Incidence Number
Total Pop


Step 2: Calculate C & D




Disease (+)
Disease (-)


Test (+)
90%
5%
All Test Positive


Test (-)
10%
95%
All Test Negative




Incidence Number
Popul

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