Decision Analysis and Benefit : Risk Determinations · Decision Analysis and Benefit : Risk...
Transcript of Decision Analysis and Benefit : Risk Determinations · Decision Analysis and Benefit : Risk...
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Decision Analysis
and
Benefit : Risk Determinations
Telba Irony, PhD
Chief, General and Surgical Devices Branch
Division of Biostatistics
Office of Surveillance and Biometrics
Center for Devices and Radiological Health
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The Science of Small Clinical Trials
November 27-28, 2012
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Outline
1. Introduction: What is Decision Analysis?
2. A Decision Problem
3. Reducing Uncertainty
4. Value of Information
5. Pre-market Decisions
6. Factors for Benefit : Risk Determinations
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1. Introduction: What is Decision Analysis?
• A set of tools to describe, inform and analyze
decision making in the presence of uncertainty
• A method of communication and insight by making
“values” (objectives, preferences) that drive
decisions explicit (transparency)
• A way of combining values and data, accounting for
differences in values and uncertain information
• A method that allows for the determination of
where resources should be allocated to obtain
information that has the highest value for decision
making important for rare diseases 3
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Decision Analysis and Small Clinical Trials
Decision Analysis can be used to weigh the benefit of obtaining more information against the loss incurred to obtain such information
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Larger sample size, better trial
Gains: reduction in the uncertainty
Losses: delay in approval
patients may die or suffer irreversible
damage waiting for treatment
etc.
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Why is it hard to make decisions?
Preferences: we may not know what we prefer
Most decisions need to be made in the presence of
uncertainty
It is difficult to “formulate” the decision problem.
If we can’t formulate it, we cannot solve it
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We could use our guts to make decisions……..
Or our brains……..
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A patient has a huge headache and goes to the doctor fearing
he has a brain tumor.
Does the patient need surgery?
Decisions, decisions…..
Unknown State of Nature: presence or absence of tumor
D+: Patient has a brain tumor
D- : Patient does not have a brain tumor
2. A Decision Problem
Uncertainty…
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Formulation of a Decision Problem
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How would mathematical decision making work in the
patient’s situation?
6 Steps for the formal decision making process
1. Identify all possible outcomes (States of Nature)
• D+ : Patient has a tumor
• D- : Patient does not have a tumor
2. Identify all possible actions or decisions
• Operate
• Don’t Operate
• (Collect more information: use a diagnostic test)
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3. Quantify the uncertainty about the outcomes via
probabilities:
• P(D+) : Probability that the patient has a tumor
• P(D-) : Probability that he doesn’t have a tumor
4. List all possible consequences
See the decision tree….
The doctor examines the patient and collects the
patient history. He makes an assessment based on
his knowledge and on the prevalence of that tumor
in such patients :
P(D+) = 40% P(D-) = 60%
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(more information)
yes
no
Tumor
yes
no
Tumor
Operate
yes
no
Good
Bad
Terrible
Great!
Consequences Decision Tree
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5. Quantify the relative preferences among the
consequences through numerical values (utilities)
Do we know what we prefer?
Great! Good Bad Terrible
Subjective!
Do we know how much we prefer one consequence as
compared to others?
My preferences
Subjective!
Establish a scale
Best consequence: Max: 10 “utiles”
Worst consequence: Min: 0 “utiles”
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6. Choose the Decision with the Maximum Expected
Utility
Great! Good Bad Terrible
10 8 2 0
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6. Calculating the Decision with the Maximum
Expected Utility
8 x 0.4 + 2 x 0.6 = 4.4 utiles
yes
no
Tumor
yes
no
Tumor
Operate
yes
no
Good
Bad
Terrible
Great! 10
8
2
0
U (Operate) = U (Good) P (Tumor) + U (Bad) Pr (No Tumor)
U (Operate) = 8 x Pr (D+) + 2 x Pr (D-) =
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U (Don’t Operate) = U (Great) P(D-) + U (Terrible) P(D+)
Optimal Decision: Don’t Operate!
yes
no
Tumor
yes
no
Tumor
Operate
yes
no
Good
Bad
Terrible
Great!
U (Operate) = 4.4 U (Don’t Operate) = 6
10
8
0
2
U (Don’t Operate) = 10 x 0.6 + 0 x 0.4 = 6 (“utiles”)
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Diagnostic test? Sample more?
What is the probability that the patient has a tumor
given his test result?
3. Reducing Uncertainty
The doctor made prior probability assessments:
P(D+) = 40% ; P(D-) = 60%
Question
Is there value in gathering more information?
Sensitivity of the Test: Probability of a true positive
Specificity of the Test: Probability of a true negative
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Assume the patient tests Positive: T+
What is the probability that the patient has the tumor
given he tested positive?
Bayes Theorem
Bayes Theorem relates:
Sensitivity: probability of testing + given he has tumor
with
Positive predictive value: probability of having a tumor
given tested +
P(T+|D+) with P(D+|T+)
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Bayes Theorem
Prior probability of the tumor: P(D+) = 0.4
Sensitivity of the Test: 99%
Specificity of the Test: 90%
Patient tests Positive: T+
D-)P(D-)|P(T))P(DD|P(T
))P(DD|P(T )T|P(D
Probability he has a tumor given he tested positive = 87%
Plug into the formula, a.k.a.
Bayesian crank …...
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Back to the Decision Problem
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6 Steps
1. Possible outcomes (states of nature) • D+ : Patient has a tumor
• D- : Patient does not have a tumor
2. Actions or decisions • Operate
• Don’t Operate
• Collect more information
3. Quantify the uncertainty via probabilities
• Probability that he has a tumor given T+: 87%
• Probability that he doesn’t have a tumor given T+: 13%
4. List all possible consequences (decision tree)
5. Quantify the relative preferences through utilities (values)
6. Choose the Decision with the maximum expected utility
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6. Calculating the Decision with the Maximum Expected Utility
U (Operate |T+) = U (Good) P (D+|T+) + U (Bad) Pr (D-|T+)
U (Operate |T+) = 8 x 87% + 2 x 13% = 7.22 (“utiles”)
yes
no
Tumor
yes
no
Tumor
Operate
given T+
yes
no
Good
Bad
Terrible
Great! 10
0
2
8
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U(Don’t Operate |T+)=U(Great) P(D-|T+) + U(Terrible) P(D+|T+)
U(Don’t Operate |T+) = 10 x 13% + 0 x 0.77% = 1.3 (“utiles”)
yes
no
Tumor
yes
no
Tumor
Operate
given T+
yes
no
Good
Bad
Terrible
Great! 10
0
2
8
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U (Operate |T+) = 8 x 87% + 2 x 13% = 7.22
U (Don’t Operate |T+) = 10 x 13% + 0 x 0.77% = 1.3
Optimal Decision: Operate!
Same calculations as before with “less uncertainty”,
i.e. with the probabilities updated by the test result
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Value of Information (T+) =
U(Operate |tested T+) - U(Don’t Operate |no test)
=
7.22 – 6 = 1.22 (“utiles”)
4. Value of Information
4.1. Value of Information for a positive test
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4.2. Value of Information for a negative test
If the patients tests negative (T-)
Apply Bayes Theorem
Probability he has the tumor when the test is neg. 0.74%
Probability he does not have a tumor when the test is neg. 99.26%
10
0
2
8 yes
no
Tumor
yes
no
Tumor
Operate
given T-
yes
no
Good
Bad
Terrible
Great!
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U(Operate |T-) = 8 x P(D+|T-) + 2 x P(D-|T-) = 2.04
U(Don’t Operate |T-)= 0 x P(D+|T-) + 10 x P(D-|T-)= 9.92
Optimal Decision: Don’t Operate!
Value of Information (T-) =
U(Don’t Operate |tested T-) - U(Don’t Operate |no test) =
9.92 – 6 = 3.92 (“utiles”)
Calculate the utility of each decision with updated probabilities
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Expected information for the test: before we see the
test result
Value of Information (T) =
Value Info (T+) x P(T+) + Value Info (T-) x P(T-)
Value of Information of the Test
In this case:
Value Info(T) = 1.22xP(T+) + 3.92xP(T-) = 2.69 (“utiles”)
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Test?
Whole Problem:
Sequential Decisions
Test Result yes
no
no
yes
no
yes Good
Bad
Tumor
Operate
yes Terrible
Great!
Tumor
no
yes
yes Good
Bad
Tumor
Operate
no
yes
no
Terrible
Great!
Tumor
no
T-
yes
T+
yes Good
Bad
Tumor
Operate
no
yes
no
Terrible
Great!
Tumor
no
yes
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5. Pre-market decisions
Possible decisions
Approve a device
Don’t approve a device
Get more information (other trial: more sample)
Elicit utilities (values) from stakeholders (the
Center, Physicians, Patients) to establish the
threshold for approval versus non-approval: some
loss in effectiveness may (or may not) compensate
a certain gain in safety.
To make pre-market decisions we need to:
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Different Utility Assessments
0
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0.9
1
0.05
0.15
0.25
0.35
0.45
0.55
0.65
0.75
0.85
0.95
Pr(safe|data)
Pr(
effe
ctiv
e|d
ata
)
Device treatments
1. Life threatening
disease: no
alternative is
available
Idea: Utility Assessments and Thresholds for Approval
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Different Utility Assessments
0
0.1
0.2
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0.8
0.9
1
0.05
0.15
0.25
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0.45
0.55
0.65
0.75
0.85
0.95
Pr(safe|data)
Pr(
effe
ctiv
e|d
ata
)
Device treatments
1. Life threatening
disease: no alternative is
available
2. Life threatening
disease: alternative is
available
Idea: Utility Assessments and Thresholds for Approval
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Different Utility Assessments
0
0.1
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0.8
0.9
1
0.05
0.15
0.25
0.35
0.45
0.55
0.65
0.75
0.85
0.95
Pr(safe|data)
Pr(
effe
ctiv
e|d
ata
)
Device treatments
1. Life threatening
disease: no alternative is
available
2. Life threatening
disease: alternative is
available
3. Other devices exist but
nothing works well
(osteoarthritis)
Idea: Utility Assessments and Thresholds for Approval
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Different Utility Assessments
0
0.1
0.2
0.3
0.4
0.5
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0.8
0.9
1
0.05
0.15
0.25
0.35
0.45
0.55
0.65
0.75
0.85
0.95
Pr(safe|data)
Pr(
effe
ctiv
e|d
ata
)
Device treatments
1. Life threatening
disease: no
alternative is
available
2. Life threatening
disease: alternative is
available
3. Other devices exist
but nothing works
well (osteoarthritis)
4. Several good
alternatives exist
(stricter)
Idea: Utility Assessments and Thresholds for Approval
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Factors to Consider for Benefit - Risk Determinations
(Medical Device Premarket Approval and De Novo Classifications)
http://www.fda.gov/downloads/MedicalDevices/DeviceRegulat
ionandGuidance/GuidanceDocuments/UCM296379.pdf
Guidance Document
issued on March 28, 2012
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• Benefits
Type of benefit
Magnitude of benefit
Probability of patient experiencing the benefit
Duration of benefit
• Risks
Extent (severity, types, numbers and rates) of the harmful event
Probability of a harmful event
Duration of the harmful event
For diagnostics: risks of false positives and false negatives
Factors
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• Uncertainty
Design, conduct, quality of clinical studies
Generalization of results
• Severity and chronicity of the disease
• Patient tolerance for risk and perspective on benefit
• Availability of alternative treatments
• Risk mitigation
• Post-market information (what can be deferred?)
• Novel technology for unmet medical need
Additional Factors
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Thank you
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Question 1
a. Bayes Theorem is the tool to update current information in the
light of new data obtained from a clinical trial.
b. Additional patients in a trial may reduce uncertainty and make
decision making easier.
c. The value of information obtained from 10 more subjects can be
calculated when one uses Bayesian statistics.
d. A positive diagnostic test dramatically increases the probability
that a patient has the disease being tested for.
e. If a diagnostic test does not reduce uncertainty, it has no value
for decision making
(1) a, b, and d are true
(2) c, d, and e are false
(3) All statements are true
(4) a is true and d is false
(5) a is true and c is false
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a. A small trial is advised when the loss from the delay of waiting for
a larger sample is greater than the value of information obtained
from more subjects.
b. Decision Analysis is an algorithm that tells what decision should
be made.
c. Public Health decision making involves preferences, values, and
subjectivity.
d. Waiting to make a decision is itself a decision that involves risks
and benefits.
e. Decision analysis is a set of mathematical tools that specify a
process to make decisions.
Question 2
(1) a, b, and d are true
(2) d and e are false
(3) All are true
(4) a is true and d is false
(5) a is true and b is false