Bayes' theorem
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Guhjy (對話 | 貢獻)
(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...)
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當前修訂版本
- 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
