Post on 22-Jul-2020
Adaptive biomarker-driven designs in Phase III clinical trials
Alex Dmitrienko Vice President Center for Statistics in Drug Development Quintiles Innovation
KU Medical Center Department of Biostatistics April 18, 2014
Outline
– Personalized medicine approach
• Development of tailored therapies
– Clinical trial designs
• Subpopulation-only designs versus multi-population designs
– Case study
• Oncology Phase III trial
– Adaptive biomarker-driven clinical trial designs
• Development and optimization of complex data-driven decision
rules
2
Personalized medicine approach
– Tailored therapies
• Novel therapies that target subgroups of patients with certain
characteristics
• Patient characteristics include demographic variables, clinical
variables, gene or protein expression markers, etc
– Regulatory guidance documents
• U.S. (Food and Drug Administration): Enrichment strategies for
clinical trials to support approval of human drugs and biological
products (December 2012)
• Europe (Committee for Medicinal Products for Human Use:
Guideline on the investigation of subgroups in confirmatory clinical
trials (January 2014)
3
Clinical trial designs
– Patient populations
• Overall population (OP)
• Marker-positive subgroup (M+)
• Marker-positive subgroup (M-)
– Therapeutic benefit
• Greater therapeutic benefit is expected in the marker-positive
subgroup
4
Clinical trial designs
– Subpopulation-only designs
• Enrollment is restricted to M+ (only marker-positive patients are
enrolled) and M- is not studied (Freidlin, McShane and Korn, 2010)
• Also known as biomarker-enriched designs
– Multi-population designs
• All patients are enrolled and treatment effect is examined in OP as
well as M+ (Millen et al., 2012)
• Also known as biomarker-stratified designs
5
Subpopulation-only designs
– Breast cancer example
• Herceptin (trastuzumab) for treatment of breast cancer (Romond et
al., 2005)
• Important marker: Amplified HER2 gene
• Efficacy in marker-positive patients (with HER2-positive tumors)
was established and marker-negative patients were not enrolled
• Marker-negative patients were denied access to potentially
beneficial treatments but Herceptin is likely to have a positive effect
in classifier-negative patients (Paik et al., 2008)
– Regulatory position
• Desirable to evaluate the efficacy and safety of new treatments in
both marker-positive and marker-negative patients (FDA
enrichment guidance)
6
Case study
– Phase III clinical trial
• Patients with a rare type of cancer
– Treatment comparison
• Experimental therapy plus chemotherapy versus placebo plus
chemotherapy
– Primary endpoint
• Overall survival (OS)
– Potential predictive biomarker
• Biomarker (protein expression marker) discovered in Phase II trial
• Believed to be predictive of treatment response
7
Adaptive biomarker-driven trial design
– Two-stage design
• Interim analysis to review the safety and efficacy data
– Stage I (before the interim analysis)
• Multi-population trial design (both marker-negative and marker-
positive patients are enrolled)
– Stage II (after the interim analysis)
• Trial design may be adaptively modified at the interim analysis to
improve the overall probability of success
8
Two-stage design
9
Futility stopping rules, population selection rules and sample size
adjustment rules will be applied at the interim analysis
Stage I Stage II
Mark
er
po
sitiv
e
Mark
er
ne
ga
tive
???
Interim analysis
Decision rules at the interim analysis
– Futility stopping
• Evaluate futility in the overall population and marker-positive
subgroup
– Population selection
• Select the overall population or marker-positive subgroup for
Stage II
– Sample size adjustment
• Increase the sample size in Stage II if necessary
10
Decision rules at the interim analysis
11
Summary of all decision rules (↑ increase the sample size)
↑ M+
Stop
Low
Pro
ba
bili
ty o
f su
cce
ss in
M+
Hig
h
Probability of success in OP
Low High
M+
Mediu
m
↑ OP
↑ OP M+
OP
Medium
Futility stopping rules
– Futility stopping rules
• Futility stopping rules will be implemented based on predicted
probability of success at the interim analysis
– Commonly used approaches
• Frequentist (conditional power)
• Bayesian (predictive power)
• Bayesian (predictive probability)
12
Conditional power
13
Conditional power function CPn(d)=P(ZNza|Zn,d)
• Likelihood of a statistically significant result given the interim data
• Zn, Test statistic at the interim analysis
• ZN, Test statistic at the planned end
• d, Assumed treatment difference
Observed data Future data are generated
from assumed
treatment difference
Interim look Start End
Sta
tistica
lly
sig
nific
ant
diffe
rence
Predictive power
14
Predictive power function PPn=CPn(d)f(d|Zn)dd
• Likelihood of a statistically significant result given the interim data
averaged over the posterior distribution of d
• f(d|Zn), Posterior density of treatment difference d given the interim
data
Observed data Future data are
generated from
posterior distribution
Interim look Start End
Prior
d
istr
ibu
tion
Poste
rior
dis
trib
ution
Sta
tistically
sig
nific
ant
diffe
rence
Predicted probability of success
– Outcome 1: Low probability of success
• Discontinue enrollment due to futility if the predicted probability of
success at the planned study end is low (e.g., less than 30%)
– Outcome 2: Medium probability of success
• Apply the population selection and sample size adjustment rules if
the predicted probability of success at the planned study end is
medium (e.g., 30-70%)
– Outcome 3: High probability of success
• Apply the population selection and sample size adjustment rules if
the predicted probability of success at the planned study end is
high (e.g., greater than 70%)
15
Population selection rules
16
Since greater benefit is expected in M+, predicted probability of success
in OP is lower than in M+
Low
Pro
ba
bili
ty o
f su
cce
ss in
M+
Hig
h
Probability of success in OP
Low High
Mediu
m
Medium
NA
NA NA
Population selection rules
17
No group is selected for Stage II (futility rule is met in both OP and M+)
because the success probability in M+ is low
Stop
Low
Pro
ba
bili
ty o
f su
cce
ss in
M+
Hig
h
Probability of success in OP
Low High
Mediu
m
Medium
Subgroup selection rules
18
OP is selected for Stage II because the success probability in OP is
comparable to that in M+ (non-informative marker)
Low
Pro
ba
bili
ty o
f su
cce
ss in
M+
Hig
h
Probability of success in OP
Low High
Mediu
m
OP
OP
Medium
Population selection rules
19
M+ is selected for Stage II because the success probability in M+ is
medium or high and no effect in M-
M+
Low
Pro
ba
bili
ty o
f su
cce
ss in
M+
Hig
h
Probability of success in OP
Low High
M+
Mediu
m
Medium
Population selection rules
20
M+ and OP are selected for Stage II because the success probability is
high in M+ and medium in OP
Low
Pro
ba
bili
ty o
f su
cce
ss in
M+
Hig
h
Probability of success in OP
Low High
Mediu
m
OP M+
Medium
Population selection rules
21
Summary of population selection rules
M+
Stop
Low
Pro
ba
bili
ty o
f su
cce
ss in
M+
Hig
h
Probability of success in OP
Low High
M+
Mediu
m
OP
OP M+
OP
Medium
Potential regulatory claims
– Broad claim
• Treatment effectiveness in the overall population only (OP only)
– Restricted claim
• Treatment effectiveness in the marker-positive subgroup only (M+
only)
– Enhanced claim
• Treatment effectiveness in the overall population as well as marker-
positive subgroup (OP and M+)
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Sample size adjustment rules
– Promising zone approach
• Adjust the sample size in the overall population or marker-positive
subgroup in Stage II based on the predicted probability of success
– Outcome 1: Original sample size
• in M+ or OP if the predicted probability of success is high (e.g.,
greater than 70%)
– Outcome 2: Increase the sample size
• Increase the sample size in M+ or OP in Stage II if the predicted
probability of success is medium [promising zone] (e.g., 30-70%)
• The sample size will be adjusted to increase the predicted
probability of success to 70%
• Sample size increase will be capped (e.g., at 30%)
23
Sample size adjustment rules
24
Use the original sample size in M+ or OP since the predicted probability
of success is high
M+
Stop
Low
Pro
ba
bili
ty o
f su
cce
ss in
M+
Hig
h
Probability of success in OP
Low High
M+
Mediu
m
OP
OP M+
OP
Medium
Sample size adjustment rules
25
Increase the sample size in M+ since the success probability is medium
M+
Stop
Low
Pro
ba
bili
ty o
f su
cce
ss in
M+
Hig
h
Probability of success in OP
Low High
M+
Mediu
m
OP
OP M+
OP
Medium
Sample size adjustment rules
26
Increase the sample size in OP since the predicted probability of
success is medium
M+
Stop
Low
Pro
ba
bili
ty o
f su
cce
ss in
M+
Hig
h
Probability of success in OP
Low High
M+
Mediu
m
OP
OP M+
OP
Medium
Final decision rules
27
Summary of all decision rules at the interim analysis (↑ increase the
sample size)
↑ M+
Stop
Low
Pro
ba
bili
ty o
f su
cce
ss in
M+
Hig
h
Probability of success in OP
Low High
M+
Mediu
m
↑ OP
↑ OP M+
OP
Medium
Final analysis
– Impact of data-driven decisions
• Virtually all adaptive (data-driven) decisions at an interim analysis
change the distribution of the treatment effect test statistics in OP
and M+ at the final analysis
• Overall Type I error rate is inflated if no adjustment is introduced
– Adjustment for data-driven changes
• Combination function approach (Brannath, Posch and Bauer, 2002)
will be applied to adjust the test statistics in OP and M+ and protect
the overall Type I error rate
– Adjustment for multiple comparisons
• Fallback procedure (Dmitrienko and D’Agostino, 2013) will be
applied to control the Type I error rate if two population are selected
for the final analysis (OP and M+)
28
Adaptive biomarker-driven clinical trial designs
– Design parameters
• Timing of the interim analysis
• Cutoff points used in the population selection and sample size
adjustment rules
• Cap used in the sample size adjustment rule
– Clinical trial optimization
• Comprehensive quantitative evaluation of candidate trial designs
was performed to optimally select individual designs parameters
• Operating characteristics of over 50 different trial designs were
evaluated to select the final design
29
Summary
– Adaptive designs
• Adaptive designs with complex decision rules are becoming more
widespread in Phase III trials
• Powerful statistical methods are available to support complex data-
driven decision making rules
– Operational issues
• Overall decision making process must be carefully planned, e.g.,
set up an independent data monitoring committee and data
analysis group, set up a flexible randomization system, develop site
expansion strategies, etc
30
References
Brannath, W., Posch, M., Bauer, P. (2002). Recursive combination tests.
Journal of the American Statistical Association. 97, 236-244.
Dmitrienko, A., D'Agostino, R.B. (2013). Tutorial in Biostatistics: Traditional
multiplicity adjustment methods in clinical trials. Statistics in Medicine. 32,
5172-5218.
Freidlin, B., McShane, L.M., Korn, E.L. (2010). Randomized clinical trials with
biomarkers: Design issues. Journal of National Cancer Institute. 102, 152-
160.
Millen, B., Dmitrienko, A., Ruberg, S., Shen, L. (2012). A statistical framework
for decision making in confirmatory multipopulation tailoring clinical trials.
Drug Information Journal. 46, 647-656.
Paik, S et a. (2008). HER2 status and benefit from adjuvant trastuzumab in
breast cancer. New England Journal of Medicine. 358, 1409-1411.
Romond, E.H. et al. (2005). Trastuzumab plus adjuvant chemotherapy for
operable HER2-positive breast cancer. New England Journal of Medicine.
353, 1673-1684. 31