Thank you for that.
Do you want to take a crack at the first part of the assignment as well? :)
Best Wishes
Paper Tiger
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The math is correct and the raw data was extrapolated from quarterly SEC filings from a Nasdaq listed biotech co.
The revenues in my table are solely product revenues, as there were some additional revenues (presumably grants and the like), and this particular company in question seemed happy to disclose their annual test numbers so I can only presume that the variable in question is their pricing.
Potentially as the test gained more commercial/market recognition they were able to negotiate more favourable pricing.
Assuming that the test/revenue numbers are correct then their test price increased as follows:
Y1 - $689
Y2 - $1,862
Y3 - $2,566
Y4 - $2,741
Y5 - $2,990
Y6 - $3,053
Y7 - $3,103
Y8 - $3,133
I know you're a finance guy so I would have thought you would have picked that up ;)
To assist those interested.
10.3 - Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value
https://onlinecourses.science.psu.ed...6/table_01.pngIn this example, two columns indicate the actual condition of the subjects, diseased or non-diseased. The rows indicate the results of the test, positive or negative.
Cell A contains true positives, subjects with the disease and positive test results. Cell D subjects do not have the disease and the test agrees.
A good test will have minimal numbers in cells B and C. Cell B identifies individuals without disease but for whom the test indicates 'disease'. These are false positives. Cell C has the false negatives.
If these results are from a population-based study, prevalence can be calculated as follows:
- Prevalence of Disease= Tdisease/ Total × 100
The population used for the study influences the prevalence calculation.
Sensitivity is the probability that a test will indicate 'disease' among those with the disease:
- Sensitivity: A/(A+C) × 100
Specificity is the fraction of those without disease who will have a negative test result:
- Specificity: D/(D+B) × 100
Sensitivity and specificity are characteristics of the test. The population does not affect the results.
A clinician and a patient have a different question: what is the chance that a person with a positive test truly has the disease? If the subject is in the first row in the table above, what is the probability of being in cell A as compared to cell B? A clinician calculates across the row as follows:
- Positive Predictive Value: A/(A+B) × 100
- Negative Predictive Value: D/(D+C) × 100
Positive and negative predictive values are influenced by the prevalence of disease in the population that is being tested. If we test in a high prevalence setting, it is more likely that persons who test positive truly have disease than if the test is performed in a population with low prevalence..
Let's see how this works out with some numbers...
Hypothetical Example 1 - Screening Test A
https://onlinecourses.science.psu.ed...6/table_02.png100 people are tested for disease. 15 people have the disease; 85 people are not diseased. So, prevalence is 15%:
- Prevalence of Disease:
Tdisease/ Total × 100,
15/100 × 100 = 15%
Sensitivity is two-thirds, so the test is able to detect two-thirds of the people with disease. The test misses one-third of the people who have disease.
- Sensitivity:
A/(A + C) × 100
10/15 × 100 = 67%
The test has 53% specificity. In other words, 45 persons out of 85 persons with negative results are truly negative and 40 individuals test positive for a disease which they do not have.
- Specificity:
D/(D + B) × 100
45/85 × 100 = 53%
The sensivity and specificity are characteristics of this test. For a clinician, however, the important fact is among the people who test positive, only 20% actually have the disease.
- Positive Predictive Value:
A/(A + B) × 100
10/50 × 100 = 20%
For those that test negative, 90% do not have the disease.
- Negative Predictive Value:
D/(D + C) × 100
45/50 × 100 = 90%
Now, let's change the prevalence..
Hypothetical Example 2 - Increased Prevalence, Same Test
This time we use the same test, but in a different population, a disease prevalence of 30%.
- https://onlinecourses.science.psu.ed...6/table_03.pngPrevalence of Disease:
Tdisease/ Total × 10
30/100 × 100 = 30%
We maintain the same sensitivity and specificity because these are characteristic of this test.
- Sensitivity:
A/(A + C) × 100
20/30 × 100 = 67%
- Specificity:
D/(D + B) × 100
37/70 × 100 = 53%
Now let's calculate the predictive values:
- Positive Predictive Value:
A/(A + B) × 100
20/53 × 100 = 38%
- Negative Predictive Value:
D/(D + C) × 100
37/47 × 100 = 79%
Using the same test in a population with higher prevalence increases positive predictive value. Conversely, increased prevalence results in decreased negative predictive value. When considering predictive values of diagnostic or screening tests, recognize the influence of the prevalence of disease. The figure below depicts the relationship between disease prevalence and predictive value in a test with 95% sensitivity and 95% specificity:
https://onlinecourses.science.psu.ed...ence_graph.gifRelationship between disease prevalence and predictive value in a test with 95% sensitivity and 85% specificity.https://onlinecourses.science.psu.ed...n05/try_it.gifThink About It!
(From Mausner JS, Kramer S: Mausner and Bahn Epidemiology: An Introductory Text. Philadelphia, WB Saunders, 1985, p. 221.)
Guess the answer lies in here somewhere. Still working on it.
Initial feeling is that perhaps some of the later test results that are coming to hand may have lesser prevalence and are now being included. This could possibly account for the altered number if they are vastly different in prevalence from the initial study.
Well, the video I mentioned earlier states that out of 485 patients in the test 66 had urothelial carcinomas.
Best Wishes
Paper Tiger
Yes, I understand that, but this gives you the prevalence of the disease only within that 485 presenting with Haematuria. Is this group representative? Are there other factors that come into play when calculating the PPV? Just askin....