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Probability
Ch 6, Principle of BiostatisticsPagano & Gauvreau
Prepared by Yu-Fen Li
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Operations on Events
• a Venn diagram is a useful device for depicting the relationships among events
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A ∩ B“both A and B”
A ∪ B“A or B”
Ac or ,“not A”
Probability
• The numerical value of a probability lies between 0 and 1.
• We have
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The additive rule of probability
The additive rule of probability
• For any two events A and B
– If A and B are disjoint (mutually exclusive)
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𝑃 (𝐴∪𝐵)=𝑃 (𝐴)+𝑃 (𝐵)−𝑃 (𝐴∩𝐵)
𝑃 (𝐴∪𝐵)=𝑃 (𝐴)+𝑃 (𝐵)
The additive rule of probability
• The additive rule can be extended to the cases of three or more mutually exclusive events– If A1, A2, · · · , and An are n mutually exclusive
events, then
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A1
A2A4
A5A3
A8A6
A7
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Joint and Marginal Probabilities
• Joint probability is the probability that two events will occur simultaneously.
• Marginal probability is the probability of the occurrence of the single event.
A1 A2B1 a b a+bB2 c d c+d
a+c b+d n
P(A1)
P(A2B1)
Conditional Probability
• We are often interested in determining the probability that an event B will occur given that we already know the outcome of another event A
• The multiplicative rule of probability states that the probability that two events A and B will both occur is equal to the probability of A multiplied by the probability of B given that A has already occurred
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Independence
• Two events are said to be independent, if the outcome of one event has no effect on the occurrence of the other.– If A and B are independent events,
• •
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Multiplicative rule of probability
• For any events A and B
– If A and B are independent
‘independent’ vs ‘mutually exclusive’
• the terms ‘independent’ and ‘mutually exclusive’ do not mean the same thing.– If A and B are independent and event A occurs,
the outcome of B is not affected, i.e. P(B|A) = P(B). – If A and B are mutually exclusive and event A
occurs, then event B cannot occur, i.e. P(B|A) = 0.
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Bayes’ Theorem
• If A1, A2, · · · , and An are n mutually exclusive and exhaustive events
• Bayes’ theorem states
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exhaustive
mutually exclusive
A1A2
A3
A4A5A6
A7
A8
The Law of Total Probability
• P(A)=P(A1 A2 A3 A4)∪ ∪ ∪ =P(A1) + P(A2 ) + P(A3 ) + P(A4) = 1
• P(B)=P(B∩A1) + P(B∩A2) + P(B∩A3) + P(B∩A4) =P(A1)P(B|A1) + P(A2)P(B|A2) + P(A3)P(B∩A3) + P(A4)P(B|A4)
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B ∩ ∩BB ∩ ∩B
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Examples
• For example, the 163157 persons in the National Health Interview Survey of 1980-1981 (S) were subdivided into three mutually exclusive categories:
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Examples of marginal probabilities
• Find the marginal probabilities
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Example of the additive rule of probability
• If S is the event that an individual in the study is currently employed or currently unemployed or not in the labor force, i.e. S = E1 E∪ 2 E∪ 3.
the additive rule of probability
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Example of the law of total probability
• H may be expressed as the union of three exclusive events:
the law of total probability
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Examples of conditional probabilities
• Looking at each employment status subgroup separately
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Example of Bayes’ theorem
• What is the probability of being current employed given on having hearing impairment?
Diagnostic Tests
• Bayes’ theorem is often employed in issues of diagnostic testing or screening
• Sensitivity and Specificity
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Positive and Negative Predictive Values (PPV and NPV)
• • PPV
• NPV
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Sensitivity (SE)
1-Specificity (1-SP)
A 2 x 2 table
• The diagnostic test is compared against a reference ('gold') standard, and results are tabulated in a 2 x 2 table
Test Gold StandardResults D+ D-
T+a
(TP)b
(FP)
T-c
(FN)d
(TN)
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Sensitivity = a / a+cSpecificity = d / b+d
Positive Predictive Value (PPV) = a / a+b ??
Negative Predictive Value (NPV) = d / c +d ??
Prevalence = a+c / (a+b+c+d) ??
Relationship of Disease Prevalence to Predictive Values
• The probability that he or she has the disease depends on the prevalence of the disease in the population tested and the validity of the test (sensitivity and specificity)
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Example
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D+ D- TotalT+ 20 180 200T- 10 1820 1830
Total 30 2000 2030
30 202030 30
30 20 2000 1802030 30 2030 2000
2000 18202030 2000
2000 1820 30 102030 2000 2030 30
20 / (20 10) 67%
1820 / (180 1820) 91%
20 / (20 180) 10%
1820 / (10 1820) 99.5%
SE
SP
PPV
NPV
( ) 30 / 2030 1.48%P D
Example
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D+ D- TotalT+ 67 9 76T- 33 91 124
Total 100 100 200
67% & 91%
1.48% 67% 6710%
1.48% 67% (1 1.48%) (1 91%) 76
(1 1.48%) 91% 9199.5%
(1 1.48%) 91% 1.48% (1 67%) 124
SE SP
PPV
NPV
( ) 1.48% 50%P D