0
$\begingroup$

In view of the increasing need for organ transplants( possibly because of the greater ability of the surgeons and physicians to both execute the transplant, prevent a rejection, and/or diagnose the need to transplant) how could one minimize the Type I statistical error (the possibility that while the patient is not brain dead he is diagnosed as such) when testing for brain death?

It is a waste of resources to wait for metabolic death clinging to a life that cannot be saved thence omitting the duty to save people.

Nonetheless the duty to omit killing people actively/intently is even greater. This is exactly what we are doing when we remove the life support of someone in a chronic vegetative state( we will not go to jail only because at that time as medical standards mandate we wrongly assumed/believed they were brain dead, i.e We acted according to the medical standards but nonetheless came to a wrong conclusion/diagnosis).

Being a layman( I studied(partialy, uncomplete course) medicine aspiring to be a radiation oncologist and devote myself to proton therapy and carbon-ion radiation therapy for the brain probably due to my own PMA) I am absolutely terrified in the thought of being mistakenly diagnosed brain dead while being in vegetative state. I want my body to be useful even after I die I am just reluctant to sign up for organ donation because it might put even more stress on the already hectic life of physicians and increase the probability of a False Positive. Albeit being a cancer patient I could not even donate blood.

Therefore could a straight 48-hour or 72-hour recording of an EEG with as many non-redundant and carrefuly placed electrodes prove useful in reducing false positives? Is there a better alternative. Being a sufficient condition( or as close to it as possible) with the least necessary conditions.

$\endgroup$
  • $\begingroup$ I think you mean a Type I error: the null hypothesis is that the patient is alive. A Type I error would be concluding that someone is brain dead despite them being alive: a rejection of the null hypothesis when it should not be rejected. Also called a False Positive. Is there any evidence that the type I error rate for diagnosis of brain death is anything more than negligible using current methodology? $\endgroup$ – Bryan Krause Jan 10 at 22:49
  • $\begingroup$ @BryanKrause Since we are testing for brain death I would say that the null hypothesis is whole brain death. Positive means that we did not reject the null. A rejection could not be positive semantically. As for the probability being negligible no positive probability of killing a person could be negligible. The value of lives cannot be measured. 1 death equals 1 million deaths. Omitting the duty of killing people will always carry a greater burden morally that omitting the duty of saving people. I just want the probability to be as less as possible while not waiting for metabolic death. $\endgroup$ – George Ntoulos Jan 10 at 23:03
  • $\begingroup$ That is exactly the opposite of how those terms are used in statistics. If you are testing for brain death, the null hypothesis will always be "not brain death". Positive always refers to rejecting the null. Perhaps you are confusing with the "alternative hypothesis" but we do not accept or reject the alternative, only the null. Please consult any reference on statistics. $\endgroup$ – Bryan Krause Jan 10 at 23:12
  • $\begingroup$ And of course we want the probability of incorrectly determining death to be as small as possible, but you are asking about reducing false positives. If the false positive rate is already vanishingly low, there is no way to measure whether an intervention intended to reduce the false positive rate actually does so. $\endgroup$ – Bryan Krause Jan 10 at 23:14
  • $\begingroup$ @BryanKrause My first degree was in economics thence I studied a lot of statistics. Type I error was a false negative and a Type II erro was a false positive. We choose the probability of a false negative by choosing a confidence interval and the complementary probability is the probability of false negative and minimized the probability of a false positive. 1 - Probability of Type I is the Sensitivity and 1- probability of Type II is Specificity. We favor the null and care more about a wrongful rejection that a wrongful not rejection. $\endgroup$ – George Ntoulos Jan 10 at 23:36

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.