Download Why Bayesian Approaches for CER?

Why Bayesian
How
approaches for CER?
Donald A. Berry
[email protected]
1
Outline
• Bayesian Metaanalysis & CER (ICDs)
(ICD)
• Adaptive Clinical Trials (I-SPY2)
• Modeling in CER using Multifarious
Data Sources (CISNET)
• Comparing Outcomes—Trials and
Tribulations
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Bayesian Meta-Analysis for Comparative
Effectiveness and Informing Coverage
Decisions: Application to Implantable
Cardioverter Defibrillators*
*Berry SM, Ishak J, Luce B, Berry DA. Medical Care (2010).
Disclosure: Berry Consultants contract with
Boston Scientific via UBC
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What Bayes Adds
Model sources of variation
Mortality rates over time:
changing hazards
Address possible time-
dependent effect of ICD
Cumulative meta-analysis,
illustrate effect of each
new study: When was
evidence conclusive?
Predictive probabilities
for future trials
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Studies Included
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Bayesian hierarchical modeling
of time to death
• Model 1: Proportional hazards
• Model 2: Time-dependent hazard
ratios (modeled separately by year)
• Model 3: Hierarchical treatment
effects; allow for different treatment
effects in different trials
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Hazard Rates & Survival: Models 1 & 2
Hazard rates
Survival probabilities
Control
ICD
Control
ICD
Model 1
Model 2
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Results Summary
Model 1
Model 2 (Time-dependent)
(Proportional)
RR
ICD+
RR1
RR2
RR3
RR4
RR5
0.777 1.00 0.807 0.713 0.723 0.990 0.877
(0.036)
(0.054)
(0.063)
(0.079)
(0.161)
(0.215)
0
ICD+
0.999
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Relative Risks over Time in Model 1
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Predictive Probabilities over Time
Predicted #3
Predicted #1
Observed RR
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Some Conclusions
•
•
•
•
ICD Effective: 23% hazard reduction
Effect persistent, consistent
Effect clear early on
Possible to account for changing
patient populations
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Outline
• Bayesian Metaanalysis & CER (ICD)
• Adaptive Clinical Trials (I-SPY2)
• Modeling in CER using Multifarious
Data Sources (CISNET)
• Comparing Outcomes—Trials and
Tribulations
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Current use of
Bayesian adaptive designs
•
•
•
•
MDACC (> 300 trials)
Device companies (> 25 PMAs)*
Drug companies (Most of top 40)**
CER? Not yet.
*http://www.fda.gov/MedicalDevicesDeviceRegulationandGuidance/
GuidanceDocuments/ucm071072.htm
**http://www.fda.gov/downloads/DrugsGuidanceCompliance
RegulatoryInformation/Guidances/UCM201790.pdf
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Two Recent Pubs
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A Bayesian statistical design was used with a
range in sample size from 600 to 1800 patients.
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Bayesian adaptive trials
• Stopping early (or late)
–Efficacy
–Futility
•
•
•
•
•
Dose finding (& dose dropping)
Seamless phases
Population finding
Treatment finding
Ramping up accrual
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Why?
• Smaller trials (usually!)
• More accurate conclusions and
hence better treatment for
patients, at lower cost (?)
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I-SPY 2
Slides from press conference …
(Change “Phase 2” to CER;
“experimental” to “approved”)
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Standard Phase 2 Cancer Drug Trials
Population
of patients
Population
of patients
Experimental arm
R
A
N
D
O
M
I
Z
E
Outcome:
Tumor
shrinkage?
Outcome:
Longer time
disease free
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Standard Phase 2 Cancer Drug Trials
Population
of patients
Population
of patients
Experimental drug
Consequence:
R
60-70%
Failure
A
N
of Phase
3
Trials
D
O
M
I
Z
E
Outcome:
Tumor
shrinkage?
Outcome:
Longer time
disease free
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I-SPY2 TRIAL
Population
of patients
A
R
D
A
A
N
P
D
T
O
I
M
V
I
E
Z
L
E
Y
Outcome:
Complete
response
at surgery
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I-SPY2 TRIAL
Population
of patients
A
R
D
A
A
N
P
D
T
O
I
M
V
I
E
Z
L
E
Y
Outcome:
Complete
response
at surgery
Arm 2 graduates
to small focused
Phase 3 trial
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I-SPY2 TRIAL
Population
of patients
A
R
D
A
A
N
P
D
T
O
I
M
V
I
E
Z
L
E
Y
Outcome:
Complete
response
at surgery
Arm 3 drops
for futility
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I-SPY2 TRIAL
Population
of patients
A
R
D
A
A
N
P
D
T
O
I
M
V
I
E
Z
L
E
Y
Outcome:
Complete
response
at surgery
Arm 5 graduates
to small focused
Phase 3 trial
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I-SPY2 TRIAL
Population
of patients
A
R
D
A
A
N
P
D
T
O
I
M
V
I
E
Z
L
E
Y
Outcome:
Complete
response
at surgery
Arm 6 is
added to
the mix
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Outline
• Bayesian Metaanalysis & CER (ICD)
• Adaptive Clinical Trials (I-SPY2)
• Modeling in CER using Multifarious
Data Sources (CISNET)
• Comparing Outcomes—Trials and
Tribulations
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CNN: Statistical Blitz Helps Pin
Down Mammography Benefits
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Fig. 1, Berry JNCI 1998
Updates
K
S
C
O
E
H
G
M
U
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Fig. 2, Berry JNCI 1998
U
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CISNET from NEJM
Women 40-79
Node-positive BC
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CISNET from NEJM
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Percent reductions in BC mortality
due to adjuvant Rx and screening
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Due to Adjuvant Treatment
25
E
20
R
W
M
15
S
G
D
10
5
0
0
5
10
15
20
Due to Screening
25
30
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Model(s) M
37
Accepted simulations
E
R M
G
W
S
D
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Model M: Prior to Posterior
(2 of several parameters)
“the posterior
mean effect of
tamoxifen is 0.37,
corresponding to
a 37% decrease in
the hazard of
breast cancer
mortality due to
the use of 5 years
of tamoxifen for
ER-positive
tumors in actual
clinical practice.”
Prior
Posterior
Posterior
Prior
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Breast Cancer Mortality
Future
BC
mortality
HP2010
BC Mortality / 100,000 Population
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Background
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T 14 - AI 10
T 14 - AI 40
35
T 14 - AI 10 - M Age 40+
T 14 - AI 40 - M Age 40+
30
T 14 - AI 10 - M Age 50+
T 14 - AI 40 - M Age 50+
25
20
T 40 - AI 10
HP 2010 Target
T 40 - AI 40
T 40 - AI 10 - M Age 40+
T 40 - AI 40 - M Age 40+
15
T 40 - AI 10 - M Age 50+
T 40 - AI 40 - M Age 50+
10
2000 Rate
Target
5
0
1975
Truth
1980
1985
1990
1995
2000
Year
Year
2005
2010
2015
2020
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Keeping track of costs
(and their uncertainties)
is straightforward with
Bayesian simulations
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Outline
• Bayesian Metaanalysis & CER (ICD)
• Adaptive Clinical Trials (I-SPY2)
• Modeling in CER using Multifarious
Data Sources (CISNET)
• Comparing Outcomes—Trials and
Tribulations
42
Newsweek: “What You Don’t
Know Might Kill You”
“The right doctors can make all
the difference when it comes
to treating cancer. So why
don't we know who they are?”
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Survival Outcomes,
by Disease Stage
Us:
Them:
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Local
Artifact
Truth
is no
difference
60%
longer
Comm
Central
“Will Rogers
Effect”
100%
longer
Regional Advanced
Comm
Central
33%
longer
Community
Central
(years)
survival(years)
Mediansurvival
Median
Comparing Outcomes
Overall
Stage
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Local
Comm
Central
Comm
Central
Community
Central
Median survival (years)
Using Central Staging
Regional Advanced
Overall
Stage
46
Median survival (years)
10
5
0
Local
Comm
Central
Comm
Comm
Central
Central
Community
Community
Central
Central
Using Community Staging
25
20
15
Regional Advanced
Overall
Stage
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Back to Newsweek
“A spokesperson for M.D. Anderson
Cancer Center in Houston said, ‘We
do not have outcomes data at this
time,’ while a physician there
explained that doctors don't want to
release data ‘that's difficult for
people to interpret.’”
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What would Bayes do?
Model disease stage, build
experiments to bolster weak
parts of the model.
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Outline
• Bayesian Metaanalysis & CER (ICD)
• Adaptive Clinical Trials (I-SPY2)
• Modeling in CER using Multifarious
Data Sources (CISNET)
• Comparing Outcomes—Trials and
Tribulations
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