This is a podium presentation that I gave at the 2011 World Conference on Lung Cancer in Amsterdam. The content examines the value of additional research to reduce uncertainty about ERCC1 expression based strategies to inform adjuvant chemotherapy decisions in early-stage non-small cell lung cancer.

1. Center
for
ComparaSve
EffecSveness
Research
in
Cancer
Genomics
(CANCERGEN)
THE
VALUE
OF
RESEARCH
FOR
ERCC1
EXPRESSION
TESTING
IN
STAGE
I
NON-­‐SMALL
CELL
LUNG
CANCER
Joshua
A.
Roth1,
Josh
J.
Carlson1,
LoBe
Steuten2,
ScoB
D.
Ramsey1,3,
David
L.
Veenstra1
1University
of
Washington,
Pharmaceu7cal
Outcomes
Research
&
Policy
Program
2University
of
Twente,
Health
Technology
Services
&
Research
Program
3Fred
Hutchinson
Cancer
Research
Center,
Popula7on
Health
Sciences
Program
World
Conference
on
Lung
Cancer,
Amsterdam,
Netherlands
July
6,
2011

2. Background:
ERCC1
TesSng
&
Study
ObjecSve
• Biomarkers
may
be
able
to
iden7fy
Stage
I
pa7ent
sub-­‐
groups
that
will
benefit
from
pla7num-­‐based
regimens
• ERCC1
is
a
protein
involved
in
DNA
damage
repair.
Pa7ents
with
low
ERCC1
expression
in
tumors
may
derive
survival
benefit
from
pla7num
regimens,
but
there
is
uncertainty.
• Should
addi7onal
studies
be
conducted
to
reduce
uncertainty
before
use
in
clinical
prac7ce?
• Using
a
decision
model,
we
calculated
the
value
of
addi7onal
ERCC1
tes7ng
trials
to
address
this
ques7on
Olaussen et al., NEJM, 2006; Bepler et al., American Journal of Pathology, 2011
2

3. Background:
Decision
Modeling
• Models
can
synthesize
exis7ng
clinical
and
cost
data
to
allow
comparison
of
ERCC1
and
standard
care
strategies
• Quality-­‐adjusted
life
years
(QALYs)
can
be
calculated
by
tracking
survival
and
quality
of
life
in
different
health
states
• The
net-­‐benefit
can
be
calculated
and
compared
between
strategies
to
assess
uncertainty
and
inform
decisions:
Quality- Willingness
Cost of
Net-Benefit = Adjusted * to Pay Per - Strategy
Life Years QALY
Ades et al., Medical Decision Making, 2004; Koerkamp et al., Value in Health, 2010
3

4. Background:
CalculaSng
Value
of
Research
(VOR)
• VOR
is
the
societal
value
associated
with
reducing
uncertainty,
and
making
op7mal
decisions
more
o_en
• VOR
can
be
calculated
by
examining
two
factors
over
many
model
simula7ons:
1. How
o_en
the
non-­‐op7mal
strategy
results
in
greater
net-­‐benefit
2. The
magnitude
of
the
net-­‐benefit
difference
between
strategies
in
simula7ons
where
1
occurs
Claxton et al., Health Economics, 1996; Ades et al., Medical Decision Making, 2004; Koerkamp et al., Value in Health, 2010
4

5. Methods:
Model
Inputs
&
Structure
Mean Years Overall Survival With No Chemo
No Adjuvant 5.42 (4.34-6.50) Zheng, NEJM, 2007
Chemotherapy
High ERCC1 Hazard Ratio, Chemo vs. No Chemo
Expression 1.01 (0.74-1.38) Bepler, Am J Path, 2011
Receive
Adjuvant
Chemotherapy Proportion Choosing to Receive Chemo
0.10 (0.05-0.15) Assumption
ERCC1
Expression
Testing
Mean Years Overall Survival With No Chemo
No Adjuvant 5.27 (4.22-6.32) Zheng, NEJM, 2007
Chemotherapy
Low ERCC1 Hazard Ratio, Chemo vs. No Chemo
Expression 0.83 (0.49-1.17) Ryu, Kor J Path, 2011
Receive
Resected Adjuvant
Chemotherapy Proportion Choosing to Receive Chemo
Stage I NSCLC
Patients 0.50 (0.05-0.95) Assumption
Mean Years Overall Survival With No Chemo
No Adjuvant 5.34 (4.27-6.40) Calculated From Sub-Groups
Chemotherapy
Hazard Ratio, Chemo vs. No Chemo
Standard Care
Receive 1.01 (0.78-1.30) Douillard, J Thor Onc, 2010
Adjuvant
Chemotherapy Proportion Choosing to Receive Chemo
0.10 (0.05-0.15) Gray, J Clin Onc, 2010
*All
pa7ents
are
followed
for
adverse
events,
distant
recurrence,
and
death
5

6. Results:
EsSmaSng
the
Maximum
VOR
Maximum Value of Research (VOR) at $150,000 per QALY for U.S. Population
Per Patient Maximum VOR
% of Simulations With Non-Optimal Decision: 39%
Average Consequence of Non-Optimal Decision: $9,238
Per Patient Maximum VOR: $3,603
U.S. Affected Population
% Running Count Reference
Incidence of Lung Cancer in the U.S.: 222,000 ACS, 2010
Non-Small Cell Lung Cancer: 85.0% 188,700 ACS, 2010
Stage I Non-Small Cell Lung Cancer: 16.4% 30,947 SEER, 2010
Rate of Technology Diffusion
Year 1 Cumulative Diffusion: 2.5%
Year 5 Cumulative Diffusion: 54.0%
Year 10 Cumulative Diffusion: 100.0%
10-Year U.S. Population Maximum VOR
Population Maximum VOR: $459,377,000
• The
maximum
VOR
is
large
because:
1. There
is
substan7al
uncertainty
about
the
op7mal
strategy
2. The
consequences
of
non-­‐op7mal
decisions
are
large
3. The
10-­‐year
affected
popula7on
is
large
6

7. Results:
VOR
for
Variables
in
a
Trial
$500,000,000
$459,377,000
$450,000,000
$400,000,000
Value of Research (VOR)
$350,000,000
$310,529,000
$300,000,000
$250,000,000
$200,000,000
$150,000,000
$100,000,000
$42,650,000
$50,000,000 $23,830,000
$733,000 $122,000
$0
Maximum VOR Low ERCC1 Low ERCC1 Low ERCC1 High ERCC1 Proportion
HR Long-Term Chemo HR High ERCC1
Survival Decisions
Variables
*The
values
above
reflect
the
VOR
for
a
new
trial
with
300
pa7ents
per
arm
7

8. Results:
VOR
for
Sample
Sizes
in
a
Trial
$500,000,000
$450,000,000
$400,000,000
Value of Research (VOR)
$350,000,000
VOR for
Trial
$300,000,000
$250,000,000 Maximum
VOR
$200,000,000
Cost of
$150,000,000 Trial
$100,000,000
$50,000,000
$0
0 100 200 300 400 500 600 700 800 900 1,000
New Trial Sample Size (Per Arm)
*These
values
represent
a
new
randomized
trial
to
examine
5-­‐year
overall
survival
8

9. LimitaSons
&
Conclusions
• There
is
lidle
evidence
about
chemotherapy
decisions
in
Stage
1
NSCLC,
or
in
ERCC1
strategies
• There
may
be
feasibility
issues
that
are
not
captured
in
the
model
and
could
prohibit
trials
• VOR
analysis
can
be
used
to
inform
study
design
and
priori7ze
research
investments
• Significant
societal
value
could
be
realized
through
addi7onal
studies
of
ERCC1
tes7ng
in
Stage
I
NSCLC
9

10. Acknowledgements
• David
Veenstra
• Rahber
Thariani
• Scod
Ramsey
• David
Blough
• Josh
Carlson
• Anirban
Basu
• Lode
Steuten
Supported
in
part
by:
U.S.
Na7onal
Cancer
Ins7tute,
Agency
Award
#5RC2CA148570-­‐01,
PI:
Ramsey
S
PhRMA
Founda7on
Pre-­‐Doctoral
Fellowship
in
Compara7ve
Effec7veness
AFPE
Pre-­‐Doctoral
Fellowship
in
Pharmaceu7cal
Science
10