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Topic 6 Probability. Modified from the notes of Professor A. Kuk P&G pp. 125-134. Events: passing an exam getting a disease surviving beyond a certain age treatment effective. An event may occur or may not occur. What is the probability of occurrence of an event?.
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Topic 6Probability Modified from the notes of Professor A. Kuk P&G pp. 125-134
Events: • passing an exam • getting a disease • surviving beyond a certain age • treatment effective An event may occuror may not occur. What is the probability of occurrence of an event? Use letters A, B, C, … to denote events
Operations on events 1º Intersection A = “A woman has cervical cancer” B = “Positive Pap smear test” “A woman has cervical cancer and is tested positive”
Venn Diagram S A B
2° Union • • • • e.g. 6 sided die • • • • • • • • • • • • • • • • • A=“Roll a 3” B=“Roll a 5”
Venn Diagram S A B
3° Complement “A complement,” denoted by Ac, is the event “not A.” A = “live to be 25” Ac= “do not live to be 25” = “dead by 25”
Venn Diagram S Ac A
Definitions: Null event Cannot happen --- contradiction
Mutually exclusive events: Cannot happen together: A = “live to be 25” B =“die before 10th birthday”
Venn Diagram S B A
Meaning of probability What do we mean when we say P(Head turns up in a coin toss) ? Frequency interpretation of probability Number of tosses 10 100 1000 10000 Proportion of heads .200 .410 .494 .5017
More generally, If an experiment is repeated n times under essentially identical conditions and the event A occurs m times, then as n gets large the ratio approaches the probability of A. as n gets large
For any event A Complement
Venn Diagram Repeat experiment n times Ac=n-m A=m
Mutually exclusive events If A and B are mutually exclusive i.e.cannot occur together
Venn Diagram when A and B are mutually exclusive Conduct experiment n times B=k A=m
Additive Law If the events A, B, C, …. are mutually exclusive – so at most one of them may occur at any one time – then :
In general, B A
Multiplicative rule Note:
Diagnostic tests D = “have disease” Dc =“do not have disease” T+=“positive screening result P(T+|D)=sensitivity P(T-| Dc)=specificity Note: sensitivity & specificity are properties of the test
PRIOR TO TEST P(D)= prevalence AFTER TEST: For someone tested positive, consider P(D|T+)=positive predictive value. For someone tested negative, consider P(Dc |T-)=negative predictive value. Update probability in presence ofadditional information
D T+ Dc
Using multiplicative rule prevalencex sensitivity = prev x sens + (1-prev)x(1-specifity) = positive predictive value = PPV This is called Bayes’ theorem
X-ray Tuberculosis Yes Positive 22 Negative 8 Total 30 Example: X-ray screening for tuberculosis
X-ray Tuberculosis Yes No Positive 22 51 Negative 8 1739 Total 30 1790 Example: X-ray screening for tuberculosis
X-ray Tuberculosis Yes No Positive 22 51 Negative 8 1739 Total 30 1790 Example: X-ray screening for tuberculosis
Screening for TB Population: 1,000,000
Population: 1,000,000 Prevalence = 9.3 per 100,000 No TB: 999,907 TB: 93
Population: 1,000,000 TB: 93No TB: 999,907 = 0.7333 Sensitivity T+ 68 T- 25
Population: 1,000,000 TB: 93No TB: 999,907 Specificity 0.9715 = T+ 68 T- 25 T+ 28,497 T- 971,410
Population: 1,000,000 TB: 93No TB: 999,907 T+ 68 T- 25 T+ 28,497 T- 971,410 T+ 28,565 T- 971,435
Population: 1,000,000 TB: 93No TB: 999,907 T+ 28,497 T+ 68 T+ 28,565 compared with prevalence of 0.00093
Population: 1,000,000 TB: 93No TB: 999,907 T- 971,410 T- 25 T- 971,445