04-19-2020, 06:11 AM
So if the disease prevalence (the thing you're trying to measure) is already ASSUMED to be 3% then the likelihood of a tested positive being a true positive by your formula is:
0.95 * 3 + 0.05 * (100 - 3)
= 2.85 + 4.85
= 7.7 (percent?)
Does that seem:
1) A reasonable answer? Your assumed disease prevalence is 3% and the test has a 5% error and yet you end up only 7.7% probability of your positive test result being a true positive?
2) A worthwhile test to run? As many initial antibody tests are in fact showing about 3% of people have already had covid currently, why would there be so much interest in performing such tests if those results have only a 7.7% probability of being accurate?
0.95 * 3 + 0.05 * (100 - 3)
= 2.85 + 4.85
= 7.7 (percent?)
Does that seem:
1) A reasonable answer? Your assumed disease prevalence is 3% and the test has a 5% error and yet you end up only 7.7% probability of your positive test result being a true positive?
2) A worthwhile test to run? As many initial antibody tests are in fact showing about 3% of people have already had covid currently, why would there be so much interest in performing such tests if those results have only a 7.7% probability of being accurate?