DBrower, idiot at large

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Yes, but there is absolutely no reason whatsoever to believe that the sensitivity and specificity are equal (and certainly the sensitivity is not .99 %).The original scenario I presented used assumed values to show the effect of changing one variable. There was never any claim that the asumed values were those corresponding to UCI dope tests.

I was trying to work out what DBrower, TBV, whatever was saying. I wasn't agreeing with him, just trying to work out his point. The math example I used after someone questioned what I meant by false positives going down as the percentage of the peloton doping goes up. In essence it is irrelevant because of masking techniques, the effective number of dopers can appear to drop to a low percentage (only a few ever test positive) even though perhaps 80% are doping.

There are some idiots here it seems who perhaps think I am a doping apologiost(??) for stating that I think Floyd feels wronged because he perhaps thought he should have passed the testosterone screen test, even though I believe he he is a 100% doper. No sympathy for Floyd from me. The IRMS test proved he was a doper. Just trying to work out his psychology. To me, he's acting like a criminal who has been done in based on a surprise illegal police raid on his house. He was caught red-handed but believes he shouldn't have been caught if the police followed procedures and the law. And it was just a speculation.

It started in challenge. … it end in old dilema…..in context to his post, Crank correct. …….in context to testing efficency, TDL correct……different definitions due to different endpoint concerns….. one - view of accused …. another - fair testing system to most who clean.

Check this…it clear all controversy……doper and his apologist (floyd and duckstrap) argue well agaist fans who know well. (can not post link..crazy system todayhttp://cyclingforums.com/images/smilies/cool.gif)

Check dpf thread ‘How good does the testosterone test need to be?



(http://www.hidden4u.com/index.php?q=aHR0cDovL3d3dy5kYWlseXBlbG90b25mb3J1bXMuY29tL21haW4vaW5kZXgucGhwP3Nob3d0b3BpYz0xODYx)Hey VF... what's with pretending to be Italian? Like a child playing little games of espionage on a "cycling forum"... :rolleyes:

99% sensitive means that the test will correctly identify a doper 99% of the time... and 99% specific means that the test will correctly identify a non-doper (clean) testing negative 99% of the time.

That means that due to the sensitivity of the test P(+ve test/doper) = 0.99 ... and P(-ve test/doper) = 0.01 [there are only two possible outcomes given the conditional prior event and they are mutually exclusive]

Likewise, due to the test's specificity, P(-ve test/clean) = 0.99 ... and P(+ve test/clean) = 0.01 [there are also only two possible mutually exclusive outcomes given the conditional event].
You can not use the same test to measure sensitivity and specificity. They have to be two different and independent tests. The example can be used as done in US Criminal Courts. The test is is the defendant Guity or Not Guilty (Not guilty does NOT mean innocent). The measure is beyond a shadow of doubt, so lots of criminals are declared not guilty. However, if we were to measure innocence, then we should use the standard beyond a shadow of doubt. Lots of defendants would fall in the no mans area where they are neither innocent nor guilty, which we call not guilty.

In statistics, on a test you develop the Alternative Hypothesis (which is what we are trying to prove), then the Null Hypothesis (which is what the test is measuring). Thus we reject the Null Hypothesis only if we feel certain
(.95, .99 or other set limit) and is is not true. Rejecting the Null Hypothesis when in is true (False Positives) is what we are controlling (alpha error). However, failing to reject the null hypothesis (beta error) we can't control. So in favor or calling someone Guilty, we make sure that we don't have false positives by only convicting when we are beyond a shadow of doubt.

So....what this means in cycling: There are a lot of dopers who are not being caught.

It is possible that if they administer a whole lot of tests, and that some may get a false positive. But note, that if the measure of false positive is .01 (99% accurate), that by doubling the tests and making them independent, then that math is .01 x .01 = .0001 probability of a false positive on both tests.
That would be only 1 out of 10,000. How many tests a year are they giving? If they are giving 10,000 tests a year, then you have a 50% probability of 1 false positive.

The last check in the system is the courts. Bottom line, I think that there are a lot of dopers, and that they manage thier biology to keep from testing positive. Problem for cyclists is that biology is a moving target, and especially during races, the biology will change, thus they get caught. Frankly, I think who gets caught is often those who have less money to spend to measure the biology (or those that get desparate for win and take a chance, such as FL).

You can not use the same test to measure sensitivity and specificity. They have to be two different and independent tests. But each test has a sensitivity characteristic and a specificity characteristic. You need to know both of these probability variables/characteristics for any test so that you can work out the probability of a false positive (see the Bayes' theorem equation for conditional probability that requires these two input variables). When you get a result of either positive or negative, you do not know for certain whether, it is a doper, or a clean rider behind each result. All you have is probabilities, unless you have a 100% sensitive or specific test.


It is possible that if they administer a whole lot of tests, and that some may get a false positive. But note, that if the measure of false positive is .01 (99% accurate), that by doubling the tests and making them independent, then that math is .01 x .01 = .0001 probability of a false positive on both tests.
That would be only 1 out of 10,000. How many tests a year are they giving? If they are giving 10,000 tests a year, then you have a 50% probability of 1 false positive. That would be true if the factors influencing the false positive were random/independent of the sample being tested. If the false positive had anything to do with the urine sample characteristics (ie someone's rare urine chemistry caused the test to give a false reading), then there would probably be (as a guess) a 99% chance of replicating the same false result on the B sample.

The last check in the system is the courts. Bottom line, I think that there are a lot of dopers, and that they manage thier biology to keep from testing positive. Problem for cyclists is that biology is a moving target, and especially during races, the biology will change, thus they get caught. Frankly, I think who gets caught is often those who have less money to spend to measure the biology (or those that get desparate for win and take a chance, such as FL).Yeah.. I agree with that.

The interesting thing with reference to the original TBV point, is that for whatever reason, the low number of positives (let's take testosterone as an example) indicate that the test is reading a low incidence of doping. Due to masking agents or whatever, let's say 40% of the peloton hypothetically could be using synthetic tesosterone, but the screen test is only finding the incidence at around 0.5% for instance (due to these masking techniques). Or maybe testosterone is really only used by 0.5% of pros now as there are far better doping products nowadays that perhaps aren't even being tested. In any case, at low percentages of detected positives, the chance of a false positive is much higher. Which coincides with TBV's original point.

And agreeing (perhaps/maybe if I am understanding him correctly) with one point TBV/DBrower made on his blog, does not mean I agree with everything TBV says, or even anything else he says. Just in case one of our dicks trying to sound Italian wants to run in there with a label of convenience based on zero logic.