Stephen T. Parente, Ph.D. Carlson School of Management, Department of Finance
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Transcript of Stephen T. Parente, Ph.D. Carlson School of Management, Department of Finance
University of Minnesota
Medical Technology Evaluation and Market
Research Course: MILI/PUBH 6589
Spring Semester, 2012Stephen T. Parente, Ph.D.
Carlson School of Management, Department of Finance
Lecture Overview
• Statistical Uncertainty• Baye’s Rule• Practice Exercise• Markov Modeling • Group Project work
Statistical Uncertainty
• Model Uncertainty– How do you know if the CE analysis you
have purchased are using the right model?
• Tough one!• In the Monte Carlo analysis, do any
of the draws give crazy results?
Statistical Uncertainty: Example
Model 1 Model 2
Randomness in health & cost outcomes
• Like uncertainty over parameter estimates, there may be uncertainty over outcomes and costs.
• Can use information on distribution of outcome and costs from clinical trial data or other datasets
• How might you do this? What is the goal?• Use Monte Carlo methods here• Markov Models
Randomness in health & cost outcomes: Example
Bayes’ Rule
• How should one rationally incorporate new information into their beliefs?
• For example, suppose one gets a positive test result (where the test is imperfect), what is the probability that one has the condition?
• Answer: Bayes’ Rule!• Particularly useful for the analysis of
screening but it applies more broadly to the incorporation of new information
Bayes’s Rule
• Bayes rule answers the question: what is the probability of event A occurring given information B
• You need to know several probabilities• Probability of event given new info =
F(prob of the event, prob of new info occurring and the prob. of the new info given the event)
Bayes’s Rule
• Notation:– P(A) = Probability of event A (unconditional)– P(B) = Probability of information B
occurring– P(B|A) = Probability of B occurring if A– P(A|B) = Probability of A occurring given
information B (this is the object we are interested in
• Bayes’s Rule is then:
)(
)(
BP
APABPBAP
Baye’s Rule Example
• Cancer Screening– Probability of having cancer = .01– Probability that test is positive if you have
cancer = .9– Probability of false positive = .05
• Use Baye’s rule to determine the probability of having cancer if test is positive
15.01.9.05.99.
01.9.
BAP
)(
)(
BP
APABPBAP
Baye’s Rule Another Formulation
• There is another way to express the probability of the condition using Bayes’s Rule:
• Sensitivity is the probability that a test is positive for those with the disease
• Specificity of the test is the probability that the test will be negative for those without the disease
Prob of condition = sensitivityprevalence
(sensitivityprevalence)+ (1- specificity)(1 prevalence)
Markov Modeling
• Methodology for modeling uncertain, future events in CE analysis.
• Allows the modeling of changes in the progression of disease overtime by assigning subjects to differ health states.
• The probability of being in one state is a function of the state you were in last period.
• Results are usually calculated using Monte Carlo methods.
Markov Modeling Example
• Three initial treatments for cancer—chemo, surgery and surgery+chemo.
• What is the CE of each treatment?
Surgery$
Year 1
P(1)
P(2)
P(3)
Year 2
No occurrence
Local occurrence
Metastasis
Treatment$
Treatment $$
DeathP(4)
No occurrence
Local occurrence
Metastasis
Treatment
Treatment
Death
Markov Modeling Example
Surgery
Start pop = 100
Chemo
Start pop = 100Year N
SurvivingHRQL Product N
SurvivingHRQL Product
1 95 .54 51.3 92 .39 45.1
2 87 .39 33.9 81 .32 25.9
3 75 .35 26.3 75 .38 28.5
4 53 .32 16.6 65 .39 25.4
5 35 .29 10.2 48 .35 16.8
6 12 .27 3.2 35 .29 10.2
7 3 .24 .7 22 .27 5.9
8 0 0 0 3 .24 .7
Total 142.2 158.5
Markov Modeling Example w/ Discounting
—r = .03Surgery
Start pop = 100
Chemo
Start pop = 100Year N
Surviving
HRQL Product Product with
discount
N Survivin
g
HRQL Product Product with
discount
1 95 .54 51.3 51.3 92 .39 45.1 45.1
2 87 .39 33.9 32.9 81 .32 25.9 25.1
3 75 .35 26.3 24.7 75 .38 28.5 26.7
4 53 .32 16.6 15.1 65 .39 25.4 23.2
5 35 .29 10.2 9.1 48 .35 16.8 14.9
6 12 .27 3.2 2.7 35 .29 10.2 8.8
7 3 .24 .7 0.58 22 .27 5.9 4.9
8 0 0 0 0 3 .24 .7 0.60
Total 142.2 136.6 158.5 149.6
Practice Exercise
• Use Baye’s rule to determine the probability that given a positive test for Lung Cancer.
• Find the prevalence of lung cancer from the web
• Suppose that the probability of a false positive is .005
• The probability of have lung cancer if test is positive is .95
Group Project Time