Guhjy: 新頁面: T (theory: disease or diagnosis) E (evidence): symptom, sign or finding P(T): prevalence (prior or pre-test probability) of the disease P(¬T): probability of no disease *P(E): pro...

2018-03-14T05:20:50Z

新頁面: *T (theory: disease or diagnosis) *E (evidence): symptom, sign or finding *P(T): prevalence (prior or pre-test probability) of the disease *P(¬T): probability of no disease *P(E): pro...

新頁面

*T (theory: disease or diagnosis)
*E (evidence): symptom, sign or finding
*P(T): prevalence (prior or pre-test probability) of the disease
*P(¬T): probability of no disease
*P(E): probability of the evidence
*''Conditional probabilities''
**P(E|T): ''sensitivity'' of the evidence
**P(E|¬T): "1-''specificity''" of the evidence
**P(T|E): posterior or post-test probability of T given E
*P(E|T) x P(T)=P(E and T), probability that both E and T are true
*P(E|T) x P(T)+P(E|¬T) x P(¬T)]=P(E), probability of the evidence
*Likelihood ratio (LR)
*LR for a positive result = true-positive rate / false-positive rate
*LR for a negative result = false-negative rate / true-negative rate
**True-positive rate = sensitivity
**False-positive rate = 1-specificity
**False-negative rate = 1-sensitivity
**True-negative rate = specificity
*Odds=P(T)/P((¬T)
*Thus, Bayes' theorem tells us that ''post-test odds = pre-test odds x likelihood ratio''

Bayes' theorem - 修訂歷史

Guhjy: 新頁面: *T (theory: disease or diagnosis) *E (evidence): symptom, sign or finding *P(T): prevalence (prior or pre-test probability) of the disease *P(¬T): probability of no disease *P(E): pro...

Guhjy: 新頁面: T (theory: disease or diagnosis) E (evidence): symptom, sign or finding P(T): prevalence (prior or pre-test probability) of the disease P(¬T): probability of no disease *P(E): pro...