Download Basket Design of Phase III Confirmatory Trials

Basket Designs of Phase III
Confirmatory Trials
Cong Chen
Director, BARDS, Merck & Co., Inc., Kenilworth, NJ, USA
ASA NJ Chapter Symposium, 6/17/2016
Acknowledgment
• Co-authors on the present work:
– Robert A. Beckman, Lombardi Comprehensive Cancer Center
and Innovation Center for Biomedical Informatics, Georgetown
University Medical Center
– Zoran Antonijevic, Amgen
– Rasika Kalamegham, Genentech
– Wen Li, Merck
– Nicole Li, Merck
• Pathway design subteam, Small Populations workstream, DIA
Adaptive Design Scientific Working Group (ADSWG)
• During the preparation of the slide deck, we have also received very
helpful comments from Eric Rubin and Keaven Anderson from Merck
2
Outline
• Background
• A general strategy for Phase III basket design
– Challenges and recommendations
• Pruning and pooling based on same endpoint
– Type I error control and operating characteristics
• Pruning and pooling based on different endpoints
– Type I error control and an application of special
interest
• Bias in point estimation of treatment effect
• Discussions
3
Background
4
Challenges to Oncology Drug Development
• The increasing discovery of molecular subtypes of
cancer leads to small subgroups that correspond to orphan or
“niche” indications, even within larger tumor types
• Cancer is becoming largely a collection of diseases defined by
molecular subtype with low prevalence
• Enrolling enough subjects for confirmatory trials in these
indications may be challenging
• Exclusive use of “one indication at a time” approaches will not
be sustainable
• The shift to a molecular view of cancer requires a
corresponding paradigm shift in drug development approaches
5
Advanced Approaches
• “Umbrella” trials
– One tumor type with multiple drugs and predictive biomarkers
– Subjects are matched to drugs based on predictive biomarkers
– Cooperation among multiple sponsors and hard to pull off
– Examples: BATTLE, I-SPY, Lung-MAP
• “Basket” or “bucket” trials
– Multiple tumor types with one drug and a predictive biomarker
– Intends to gain drug approval in multiple tumor types with a
common predictive biomarker under the premise that molecular
subtype is more fundamental than histology
– Single sponsor and easy to pull off
6
The First Basket: Imatinib B2225
186 subjects with
40 different
malignancies with
known genomic MOA
of imatinib target
kinases
KIT, PDGFRA, or
PDGFRB
Synovial
sarcoma
Aggressive
fibromatosis
Dermatofibrosarcoma
protuberans
1/16 (6%)
2/20 (10%)
10/12 (83%)
13 centers in consortium:
North America, Europe,
Australia
Aggressive
systemic
mastocytosis
1/5 (20%)
Imatinib 400- 800 mg BID
primary endpoint ORR
Hypereosinophilic
syndrome
Myeloproliferative
disorder
6/14 (43%)
4/7 (58%)
Led to supplemental indications for these 4 subsets after pooling with
other trials and case reports
Blumenthal. Innovative trial designs to accelerate the availability of highly effective anti-cancer therapies:
an FDA perspective, AACR 2014
7
Phase II Basket Trials to Date
• A similar design to Imatinib B2225 was endorsed at a
Brookings/Friends Conference in 2011
– Statistical design not published but likely Bayesian
• Common features:
– Single-arm trials with ORR as primary endpoint
– Intend to use pooled population for primary analysis to gain
broader indication across tumor types (individual tumor type
may not be adequately powered)
– Involve possibly transformative medicines in patients with
great unmet need and seemingly exceptionally strong
scientific rationale
– Exploratory and opportunistic in nature, rendering regulatory
decision on drug approval a non-binding “review” issue
8
A General Strategy for Phase III
Basket Design
Pathway Design Subteam
• Can we develop a generalizable confirmatory basket
design with statistical rigor that follows existing regulatory
pathways to increase drug approvability?
– Applicable not only to exceptional cases, but to all
effective medicines in any line of therapy
• This would have multiple benefits:
– Increase and accelerate access to effective medicines
for patients in niche indications
– Provide sponsors with cost-effective options for
development in niche indications
– Provide health authorities with more robust packages
for evaluation of benefit and risk
10
Study Design
• Frequentist approach for statistical design and analysis
• Randomized and controlled trials (generally preferred)
– A shared control group may be used for indications with
a common standard of care to reduce sample size
• Single arm trials may be appropriate for rare tumors (or
common tumors with low prevalence), or drugs that can
clearly demonstrate superiority to SOC without using a
concurrent control
– A high rate of durable objective response may provide
evidence for drug approvals
– Performance of SOC in biomarker selected population
is often less well-known, and patient selection bias is
unavoidable
11
General Strategy
SELECTION
PRUNING
(External Data)
PRUNING
(Internal Data)
POOLING
12
Pruning
• Initial indications are carefully selected
• Prune the “losers” using external data to avoid penalty
– Data from Phase I/II trials in same indications if available
– Data in same indications for different drugs in same class
– When accrual is much slower than expected
• Prune the “losers” using trial data at an interim analysis
– Pruning is seen as cherry-picking, but it also shares
similarity with binding futility analysis
– The bar for pruning should be carefully chosen to properly
balance the risk and balance, and should also be consulted
with regulatory agencies
– Net impact on Type I error rate and point estimation is
complicated (focus of remaining presentation)
13
Why Pruning?
• Histology can affect the validity of a molecular predictive
hypothesis, in ways often unpredictable in advance
– Vemurafenib is effective for BRAF 600E mutant
melanoma, but not for analogous colorectal cancer
(CRC) tumors (due to subsequently discovered tissue
specific feedback loops)
– Any prior assumption on treatment effect across tumor
types can be challenged
• Pruning serves as an empirical approach to separate
“winners” from “losers” (seeing is believing) although it
doesn’t guarantee to separate with 100% accuracy
– One or more bad indications can lead to a failed study
for all indications in a basket
14
Pooling
• Only “winners” are pooled in final analysis
– Sample sizes of “winners” are subject to increase to ensure
adequate power in pooled population
– Whether to pool “losers” at interim that later turn out to be
“winners” at the end is a subject beyond current scope
– In an application of special interest, a surrogate endpoint is
analyzed at interim for accelerated approval and a pooled
analysis of clinical endpoint is conducted for full approval
• Study is positive if pooled analysis of remaining indications is
positive for the primary definitive endpoint
– Some “losers” may have shown adequate activity for further
investigation in a different line of therapy or for combination
15
Consistency of Treatment Effects
• Consistency of treatment effects across tumor types in
pooled analysis are subject to regulatory review, however
no formal hypothesis testing needs to be pre-specified
– This approach is consistent with current regulatory
practice (e.g., subgroup analysis based on baseline
characteristics)
• Similar methods developed in subgroup analysis, metaanalysis or analysis of multi-regional clinical trials can be
adopted for investigation
• Graphic tools (e.g., forest plot and Galbraith plot) may be
used to assist with the decision
16
Pruning and Pooling Based
on Same Endpoint
Type I Error Control
• k tumor indications, each with sample size of N and all
with 1:1 randomization
• An interim analysis is conducted at information fraction t
for each tumor indication, and a tumor will not be included
in the pooled analysis if P-value>αt
• The pooled analysis will be conducted at α* so that the
overall Type I error is controlled at α when there is no
treatment effect for any tumor (H0)
• What is α*?
18
Solving for Adjusted Alpha (α*)
• Let Yi1 be the test statistics based on information fraction
t, and Yi2 be the test statistics based on the final analysis
of data in the i-th cohort (i=1, 2,…,k)
• Suppose that m cohorts are included in the final analysis
(m≥1), and let Vm be the corresponding test statistics. The
probability of a positive outcome in pooled analysis is:
Q0(α*|αt, m)= PrH 0 (∩{Yi1> Z1t for i=1,…,m}, ∩{Yj1< Z1t for j=m+1,…k}, Vm > Z1- α*)
or
Q0(α*|αt, m)= PrH 0 (∩{Yi1> Z1t for i=1,…,m}, Vm > Z1- α*)(1- αt) (k-m)
• α* is solved from below, where c(k, m) = k!/((k-m)!m!)
𝑘
𝑚 =1 𝑐(𝑘, 𝑚)Q0(α*|
αt, m) = α
19
Designs to Be Compared
• D0: No pruning and no change (benchmark)
• D1: No increase to sample size after pruning
• D2: Sample size in pooled analysis after pruning remains the same as
planned for the trial (SS)
• D3: Sample size for overall trial remains the same after pruning as
planned (SS)
Designs
Overall Trial
Pooled Population
D0
SS
SS
D1
<SS
<SS
D2
>SS
SS
D3
SS
<SS
20
Correlations Under Different Design Options
• Corr(Yi1, Vm)=Corr(Yi1, Yi2)/ 𝑚
• Correlation between Yi1 and Yi2 is largest under D1 and
smallest under D2
– D1: Corr(Yi1, Yi2)= 𝑡
– D2: Corr(Yi1 ,Yi2)= tm/k
– D3: Corr(Yi1 ,Yi2)= 𝑚𝑡/(𝑚𝑡 + 𝑘 1 − 𝑡 )
• Greater α* (i.e., smaller alpha penalty) is associated with
smaller correlation (i.e., the more subjects added after IA
the smaller the penalty)
21
α* Under Different Design Options
α* decreases with increasing k as expected, but its relationship with αt is complicated with
the interplay between cherry-picking and futility stopping.
22
Comparison of Operating Characteristics
• k=6 tumor indications with total planned event size (kN)
ranging from 150 to 350
– The true treatment effect is –log(0.6), or hazard ratio of
0.6 in a time-to-event trial (e.g., PFS, OS or any
endpoint acceptable to regulatory agencies)
• Pruning occurs at t=0.5 and the bar is set at αt = 0.4
– An indication is pruned if observed hazard ratio at
interim is greater than ~0.87 to 0.91 based on event
size range
– α* is 0.32% under D1, 0.50% under D2, and 0.43%
under D3
– Number of active indications (g) with target effect size
ranges from 3 to 6, with remaining ones inactive
23
Study Power When Number of Active
Indications Varies
D2 is always the most powerful. The relative performance of D0 deteriorates as g decreases.
24
Study Power and Sample Sizes Under
Different Pruning and Pooling Strategies
Power (%) for a
Positive Study
Exp. Number of Events Exp. Number of Events
for Pooled Population
for Overall Study
Planned
Events
Number of
Active
Tumors (g)
D0
D1
D2
D3
D0/D2
D1
D3
D0/D3
D1
D2
200
6
95
85
95
93
200
157
179
200
179
221
200
5
85
75
91
86
200
144
172
200
172
228
200
4
67
62
82
76
200
131
166
200
166
234
200
3
44
45
68
61
200
119
159
200
159
240
300
6
99
96
99
99
300
254
277
300
277
323
300
5
96
81
98
96
300
232
266
300
266
334
300
4
84
81
94
91
300
209
255
300
255
345
300
3
60
64
84
79
300
187
244
300
244
356
Except for g=6, D0 is inferior to D3 of same size for overall study and D2 of same size for
pooled population. It is inferior to D1 when half indications are active.
25
Numbers of Tumor Indications and Expected
True Treatment Effect in Pooled Population
Planned
Events
Number of
Active
Tumor
Indications
200
Exp. Number of Active
Tumor Indications in
Pooled Population
Exp. Total Number of
Tumor Indications in
Pooled Population
Exp. True Effect in
Pooled Population
Relative to Δ (%)
No
Pruning
With
Pruning
No
Pruning
With
Pruning
No
Pruning
With
Pruning
6
6
4.7
6
4.7
100
100
200
5
5
3.9
6
4.3
83
91
200
4
4
3.1
6
3.9
67
80
200
3
3
2.4
6
3.6
50
66
300
6
6
5.1
6
5.1
100
100
300
5
5
4.2
6
4.6
83
91
300
4
4
3.4
6
4.2
67
81
300
3
3
2.5
6
3.7
50
68
More inactive tumor indications are pruned than active ones. While exclusion of some active
indications under pruning is unfortunate, inclusion of many inactive ones without pruning defies
the principle of drug development and will be a major regulatory and reimbursement issue.
26
Application to A Single Arm Basket Trial
• Traditionally, Simon’s optimal two-stage design is used to
decide whether an investigational therapy is worth further
development in Phase III
– The design minimizes expected sample size under null
– The Type I/II error rates are often high
• A trial stops for futility after first stage if the futility bar is
met, and continues to completion otherwise, in which
case a final analysis will be conducted
• How to turn this classical design (or any two-stage
design) naturally into a confirmatory basket trial in settings
that ORR is an acceptable endpoint for drug approval?
27
A Hypothetical Example
• Ten tumor indications, each applying Simon’s optimal two-stage
design with (α, β)=(0.1, 0.1) and (p0, p1)=(0.1, 0.3)
• The first stage enrolls 12 subjects each and an indication is
dropped if there is ≤1 response. Otherwise, the indication
continues until 35 subjects are enrolled.
– No change in sample size (i.e., D1)
– An indication would be declared inactive if ≤5 responses at
final analysis based on Simon’s optimal two-stage design
• An exact calculation shows that the pooled analysis should be
conducted at α*=0.29% to keep the overall alpha at 2.5%
– E.g., min empirical RR is ~20%/25%/30% for trial to be
positive if pooled from 4/2/1 tumor indications and each
further requires >5 responses
28
Pruning and Pooling Based
on Different Endpoints
Set-up for Multiplicity Adjustment
• An intermediate endpoint may be used for filing for accelerated
approval or to terminate the study early to save cost and time
– Bar for pruning is usually higher (αt smaller) in either case
• The correlation between Yi1 (test stat based intermediate
endpoint data at interim) and Yi2 (test stat based on clinical
endpoint data at final analysis) is different from before
• The treatment effect in terms of intermediate endpoint shouldn’t
be assumed null under the null hypothesis for clinical endpoint
– Meta-analysis of trials showing no treatment effect on clinical
endpoint may be used to help find a reasonable range for
treatment effect on intermediate endpoint for derivation of an
appropriate α* that controls overall alpha in this range
30
Maximum Penalty
• The treatment effects on intermediate endpoint are
treated as nuisance parameters and the α* is chosen
such that it minimizes over (-Inf, Inf) while keeping the
Type I error controlled at α
– Minimum α* is achieved when effect sizes take same
value δ (or at same δ 𝑁𝑡 in general)
• The common δ associated with minimum α* is nondegenerate (i.e., 0, -Inf or Inf) due to the complicated
interplay between futility stopping and cherry-picking
– When δ is very large, none will be pruned (no penalty)
– When δ is very small, trial may be terminated early for
futility (no chance to even commit a Type I error)
31
Correlation between Yi1 and Yi2
• In general, the correlation may be estimated by a
bootstrapping method or any other method
– Martingale residuals may be used to construct the
correlation when both are time-to-event variables
– Estimator of α* converges to true value as long as
estimator of correlation is a consistent estimator
• In the special case when Yi1 and Yi2 are continuous
variables and interim analysis is conducted based on t
proportion of planned sample size, the correlation differs
from before by a factor of ρ where ρ is correlation
between the two endpoints based on same subjects
– Next slides are based on this setup for ease of
presentation
32
Illustration of α* at Different δ at αt=0.05 and k=6
α* is minimized at a non-degenerate delta and varies with rho
33
Minimum α* Under Different Design Options
Minimum α* decreases with increasing k or t as expected and is always less than 2.5%.
34
An Application of Special Interest
• A randomized controlled basket trial with 1:1 randomization in 6 tumor
indications, each targeting a hazard ratio of 0.5 in PFS with 90%
power at 2.5% alpha for accelerated approval
– 88 PFS events and 110 subjects planned for each indication
– PFS analysis is conducted when all are enrolled (t=1)
• D2 is applied to keep total sample size at 660 in pooled population
targeting 430 death events for full approval
– ~90% power to detect a hazard ratio of 0.7 in OS at 0.8% when ρ
is 0.5 (i.e., correlation between PFS at interim and OS at final is
0.5 m/6 if m-tumors are selected due to sample size adjustment)
– Observed hazard ratio ~0.79 or lower for a positive trial in pooled
population (vs ~0.84 under D0)
• Typical Phase III sample size but potential approval in 6 indications
35
Forest Plot of Hypothetical PFS Outcome
Tumor 1
Tumor 2
Tumor 3
Tumor 4
Tumor 5
Tumor 6
Tumors 1-5
HR=1
36
Forest Plot of Hypothetical OS Outcome
Tumor 1
Tumor 2
Tumor 3
Tumor 4
Tumor 5
Tumors 1-5
HR=1
37
Bias in Point Estimation of
Treatment Effect
Bias
• The fact that a penalty should be paid to control Type I error
implies estimate of treatment effect is potentially inflated
– The smaller the α* the greater the potential bias
• Bias is not a concern if “losers” are truly losers and “winners”
are truly winners, but a conservative assumption is that
treatment effect for clinical endpoint is the same for all and
“winners” are cherry-picked due to random highs
• Similarly to what happens in Type I error control, the treatment
effect on intermediate endpoint plays a complicated role
– When it is very large, none will be pruned (no inflation)
– When it is very small, trial may be terminated early for futility
– Maximum bias is reached at a non-degenerate point
39
Set-up for Calculation
• Again, let Yi1 be the test statistics based on intermediate
endpoint data at interim and Yi2 test statistics based on clinical
endpoint data at final analysis
• Let 𝐼𝑖 (t) be the information Yi1 is based upon where t is the
proportion of planned subjects used in interim analysis (t=1 in
last example) and let 𝛿𝑖 be the true treatment effect for i-th tumor
based on the intermediate endpoint, and
– Notice that the expectation of Yi1 is 𝛿𝑖 𝐼𝑖 𝑡
• Let 𝐽𝑖 be the information Yi2 is based upon at final analysis
– The corresponding estimated treatment effect is Yi2/ 𝐽𝑖
– The estimated treatment effect in pooled population can be
approximated with a weighted average of individual estimate
for each tumor by the information units
40
Key Steps in Calculation
• Probability of including a subset of m-tumors in final analysis
𝑃𝑟 𝑌11 𝑡 > 𝑍1−𝛼𝑡 , … , 𝑌1𝑚 𝑡 > 𝑍1−𝛼𝑡 , 𝑌1,𝑚+1 𝑡 < 𝑍1−𝛼𝑡 , … , 𝑌1𝑘 𝑡 < 𝑍1−𝛼𝑡
• Bias conditional on a i-th tumor being selected follows from below:
– Bias of Yi2 conditional on Yi1 is ρ 𝑌𝑖1 − 𝛿𝑖 𝐼𝑖 𝑡
– Yi1 is a left-truncated normal variable with mean
𝛿𝑖 𝐼𝑖 𝑡 + ϕ 𝑍1−𝛼𝑡 − 𝛿𝑖 𝐼𝑖 𝑡
1 − Φ 𝑍1−𝛼𝑡 − 𝛿𝑖 𝐼𝑖 𝑡
• The overall bias is given below
𝑘
Bias𝑎 𝑠𝑢𝑏𝑠𝑒𝑡 × 𝑃𝑟 𝑎 𝑠𝑢𝑏𝑠𝑒𝑡 𝑜𝑓 𝑚 𝑡𝑢𝑚𝑜𝑟𝑠
𝑚=1 𝑎𝑙𝑙 𝑠𝑢𝑏𝑠𝑒𝑡𝑠
– Proportional to ρ and 1/ 𝐽𝑖 , and a function of 𝑍1−𝛼𝑡 − 𝛿𝑖 𝐼𝑖 𝑡
– An estimate of bias is obtained by replacing 𝛿𝑖 with its estimate
41
Symmetry Revisited
Contour plot
Contour
plotof bias
of bias
42
• A randomized basket trial with
equal sample size in 2 tumor
indications
‒ The interim analysis is
conducted after 88 PFS
events in each cohort.
‒ To have 430 death
events in the pool,
sample size changes
after pruning (D2).
‒ Maximum bias is achieved
when δ1=δ2
‒ Same conclusion holds
for > 2 cohorts due to
symmetry (similar to
minimum α* calculation)
Last Example Revisited and Expanded
• A randomized basket trial with equal sample size in 6 tumors
–The interim analysis is conducted after 88 PFS events are
observed in each cohort
–Number of deaths is kept at 430 in pooled population (D2)
• Two scenarios
–Only those with αt <0.025 (observed hazard ratio <~0.66)
are included in pooled population
– Irrespective of filing for accelerated approval, which may
require αt <0.025, a tumor indication is included in pooled
population as long as αt <0.1 (observed hazard ratio <~0.76)
• Maximum bias is comparable to difference in empirical bar for a
positive trial between pruning and no pruning
– For ρ=0.5, max bias in log(hazard ratio) is ~0.07 (or ~0.05 in
hazard ratio when true treatment effect is ~0.8)
43
Bias at αt =0.025 in -log(hazard ratio)
Bias ~0.05 in
hazard ratio
when δ=0.5
Maximum
bias ~0.07
44
Bias at αt =0.1 in -log(hazard ratio)
Bias ~0.02 in
hazard ratio
when δ=0.5
Curve shifts
to left with
same max
45
Discussions
Summary
• The risk of a basket trial is that the trial fails for all indications at
once, but a successful trial could provide very significant
benefits to patients, drug developers, and health authorities
– Sample size adjustment after pruning mitigates the risk
– Initial emphasis should be using external data for pruning
• A basket trial with pruning of indications based on an interim
analysis is feasible and, after paying the maximum penalty, has
superior overall performance vs a basket trial without pruning
– A special application is to file accelerated approval based on
interim analysis and file full approval based on final analysis
– Bias in point estimation of treatment effect can be properly
corrected if needed
47
Ongoing Research
• The pathway design subteam is working with academic
statisticians and regulatory agencies to investigate
various issues related to statistical design, analysis and
reporting, and monitoring of confirmatory basket trials to
pave the way for incorporation into regulatory guidance
• The general framework is being applied to design and
analysis of umbrella trials
• The methodology for handling different endpoints is being
applied to design and analysis of general adaptive trials
• Presentations to clinical and scientific conferences are
underway, and multiple manuscripts are under draft
48
Key References
• Chen C, Li N, Yuan S, Antonijevic Z, Kalamegham R, Beckman RA. Statistical design and considerations of a
phase 3 basket trial for simultaneous investigation of multiple tumor types in one study. To appear in
Statistics in Biopharmaceutical Research , 2016.
• Yuan S, Chen A, He L, Chen C, Gause CK, Beckman RA. On group sequential enrichment design for basket
trials. To appear in Statistics in Biopharmaceutical Research , 2016.
• Beckman RA, Antonijevic Z, Kalamegham R, Chen C. Design for a phase 3 basket trial in multiple tumor types
based on a putative predictive biomarker. Under review for publication.
• Li, W, Chen C, Li N. Estimation of treatment effect in two-stage adaptive trials using an intermediate endpoint,
under draft.
•
•
•
•
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•
•
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•
•
•
•
•
•
Xiaoyun (Nicole) Li, and Cong Chen. Adaptive Biomarker Population Selection in Phase III Confirmatory Trials. JSM proceedings of ASA
Biopharmaceutical Section, 2015.
Chen C, Li N, Shentu Y, Pang L, Beckman RA. Adaptive Informational Design of Confirmatory Phase III Trials with an Uncertain Biomarker
Effect to Improve the Probability of Success. To appear in Statistics in Biopharmaceutical Research 2016.
Magnusson BP, Turnbull BW. Group sequential enrichment design incorporating subgroup selection. Stat Med. 2013;32(16):2695-2714.
Heinrich MC, Joensuu H, Demetri GD, Corless CL, Apperley J, Fletcher JA, et al. Phase II, open-label study evaluating the activity of imatinib
in treating life-threatening malignancies known to be associated with imatinib-sensitive tyrosine kinases. Clin Cancer Res. 2008;14(9):27172725.
Berry SM, Broglio KR, Groshen S, Berry DA. Bayesian hierarchical modeling of patient subpopulations: efficient designs of Phase II oncology
clinical trials. Clin Trials. 2013 Oct;10(5):720-34.
Simon R, Geyer S, Subramanian J, Roychowdhury S. The Bayesian basket design for genomic variant-driven phase II trials. Seminars in
Oncology, DOI: http://dx.doi.org/10.1053/j.seminoncol.2016.01.002.
Berry DA. The Brave New World of clinical cancer research: Adaptive biomarker-driven trials integrating clinical practice with clinical research.
Molecular Oncology, DOI: http://dx.doi.org/10.1016/j.molonc.2015.02.011.
Neuenschwander B, Wandel S, Roychoudhury S and Bailey S. Robust exchangeability designs for early phase clinical trials with multiple
strata. Pharmaceutical Statistics, DOI: 10.1002/pst.1730.
Barker AD, Sigman CC, Kelloff GJ, Hylton NM, Berry DA, Esserman LJ. I-SPY 2: an adaptive breast cancer trial design in the setting of
neoadjuvant chemotherapy. Clin Pharmacol Ther. 2009; 86: 97-100.
Herbst RS, Gandara DR, Hirsch FR, Redman MW, LeBlanc M, Mack OC. et al. Lung Master Protocol (Lung-MAP)-A Biomarker-Driven
Protocol for Accelerating Development of Therapies for Squamous Cell Lung Cancer: SWOG S1400. Published Online First February 13,
2015; doi: 10.1158/1078-0432.CCR-13-3473.
Kim ES, Herbst RS, Wistuba II, Lee JJ, Blumenschein GR, Jr., Tsao, A. et al. The BATTLE trial: personalizing therapy for lung cancer. Cancer
Discovery 2011; 1: 44-53.
Demetri G, Becker R, Woodcock J, Doroshow J, Nisen P, Sommer J. Alternative trial designs based on tumor genetics/pathway characteristics
instead of histology. Issue Brief: Conference on Clinical Cancer Research 2011; http://www.focr.org/conference-clinical-cancer-research-2011.
Bauer P, Koenig F, Brannath W, Posch M. Selection and bias - two hostile brothers. Stat Med. 2010 Jan 15;29(1):1-13. doi: 10.1002/sim.3716.
Wathen JK,Thall PF, Cook JD and Estey EH. Accounting for patient heterogeneity in phase II clinical trials. Statist. Med. 2008; 27:2802–2815.
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