Why Bayesian
How
approaches for CER?
Donald A. Berry
dberry@mdanderson.org
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
2
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
3
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
4
Studies Included
5
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
6
Hazard Rates & Survival: Models 1 & 2
Hazard rates
Survival probabilities
Control
ICD
Control
ICD
Model 1
Model 2
7
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
8
Relative Risks over Time in Model 1
9
Predictive Probabilities over Time
Predicted #3
Predicted #1
Observed RR
10
Some Conclusions
•
•
•
•
ICD Effective: 23% hazard reduction
Effect persistent, consistent
Effect clear early on
Possible to account for changing
patient populations
11
Outline
• Bayesian Metaanalysis & CER (ICD)
• Adaptive Clinical Trials (I-SPY2)
• Modeling in CER using Multifarious
Data Sources (CISNET)
• Comparing Outcomes—Trials and
Tribulations
12
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
13
Two Recent Pubs
14
A Bayesian statistical design was used with a
range in sample size from 600 to 1800 patients.
15
16
Bayesian adaptive trials
• Stopping early (or late)
–Efficacy
–Futility
•
•
•
•
•
Dose finding (& dose dropping)
Seamless phases
Population finding
Treatment finding
Ramping up accrual
17
Why?
• Smaller trials (usually!)
• More accurate conclusions and
hence better treatment for
patients, at lower cost (?)
18
I-SPY 2
Slides from press conference …
(Change “Phase 2” to CER;
“experimental” to “approved”)
19
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
20
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
21
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
22
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
23
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
24
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
25
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
26
Outline
• Bayesian Metaanalysis & CER (ICD)
• Adaptive Clinical Trials (I-SPY2)
• Modeling in CER using Multifarious
Data Sources (CISNET)
• Comparing Outcomes—Trials and
Tribulations
27
28
29
CNN: Statistical Blitz Helps Pin
Down Mammography Benefits
30
Fig. 1, Berry JNCI 1998
Updates
K
S
C
O
E
H
G
M
U
31
Fig. 2, Berry JNCI 1998
U
32
33
CISNET from NEJM
Women 40-79
Node-positive BC
34
CISNET from NEJM
35
Percent reductions in BC mortality
due to adjuvant Rx and screening
30
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
36
Model(s) M
37
Accepted simulations
E
R M
G
W
S
D
38
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
39
Breast Cancer Mortality
Future
BC
mortality
HP2010
BC Mortality / 100,000 Population
45
Background
40
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
40
Keeping track of costs
(and their uncertainties)
is straightforward with
Bayesian simulations
41
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?”
43
Survival Outcomes,
by Disease Stage
Us:
Them:
44
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
45
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
47
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.’”
48
What would Bayes do?
Model disease stage, build
experiments to bolster weak
parts of the model.
49
Outline
• Bayesian Metaanalysis & CER (ICD)
• Adaptive Clinical Trials (I-SPY2)
• Modeling in CER using Multifarious
Data Sources (CISNET)
• Comparing Outcomes—Trials and
Tribulations
50