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New Designs for Phase III Clinical Trials

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Korn EL et al. Clinical trial designs for cytostatic agents: Are new approaches needed? ... Rubinstein LV, Korn EL, Freidlin B, Hunsberger S, Ivy SP, Smith MA. ... – PowerPoint PPT presentation

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Title: New Designs for Phase III Clinical Trials


1
New Designs for Phase III Clinical Trials
  • Richard Simon, D.Sc.
  • Chief, Biometric Research Branch
  • National Cancer Institute
  • http//brb.nci.nih.gov

2
BRB Websitehttp//brb.nci.nih.gov
  • Powerpoint presentations reprints
  • BRB-ArrayTools software
  • Human tumor annotated gene expression data
    archive
  • Web based sample size planning
  • Phase II/III trials
  • Clinical Trials with Predictive Biomarkers
  • Development of prognostic signatures

3
Topics for Discussion
  • Integrated Phase II/III Clinical Trials
  • Using genomic predictive biomarkers in phase III
    clinical trials

4
Integrated Phase II/III Clinical Trials
  • Sally Hunsberger, Yingdong Zhao, and Richard Simon

5
  • Interpretation of single arm phase II study
    results is problematic when
  • a new drug is used in combination with other
    agents
  • or when progression free survival is used as the
    endpoint.
  • Randomized phase II studies are more informative
    for these objectives but increase both the number
    of patients and time required to determine the
    value of a new experimental agent.

6
Randomized Controlled Phase II Trial
  • Randomization to standard regimen or regimen with
    new drug
  • Endpoint is time to progression regardless of
    whether it is an accepted phase III endpoint
  • One-sided significance level can exceed .05 for
    analysis and sample size planning
  • Simon R et al. Clinical trial designs for the
    early clinical development of therapeutic cancer
    vaccines. Journal of Clinical Oncology
    191848-54, 2001
  • Korn EL et al. Clinical trial designs for
    cytostatic agents Are new approaches needed?
    Journal of Clinical Oncology 19265-272, 2001
  • Rubinstein LV, Korn EL, Freidlin B, Hunsberger S,
    Ivy SP, Smith MA. Design issues of randomized
    phase 2 trials and a proposal for phase 2
    screening trials. Journal of Clinical Oncology
    2005237199-7206.

7
  • Randomized controlled phase II trials with time
    to progression endpoint require much larger
    sample sizes and longer follow-up than
    traditional single arm phase II trials unless
  • A large treatment effect is targeted
  • Time to progressive disease is short

8
Number of Events Required for Randomized Trial
With Time to Event Endpoint
For ?0.05, ?0.20, hr1.5, E75 events are
required For ?0.10, 55 events
9
  • Randomized discontinuation trials can require
    larger sample sizes than randomized controlled
    phase II trials in some cases
  • Freidlin B and Simon R. An evaluation of the
    randomized discontinuation design. J Clin Oncol
    231-5,2005.

10
  • We compared different phase II study strategies
    for developing a new regimen compared to a
    control for improving OS
  • Perform phase III of OS if single arm phase II of
    PFS is significant
  • Perform phase III of OS if randomized controlled
    phase II of PFS is significant
  • Integrated phase II/III
  • Phase III of OS with futility analysis of PFS
  • No phase II, go directly to phase III of OS with
    futility analysis of OS
  • Comparison based on total number of patients and
    total length of time to conclusion of drug
    efficacy on overall survival.

11
Pancreatic Cancer Example
  • median OS is about 6 months.
  • Improvement in OS to 7.8 months is used for
    sizing phase III trial (hazard ratio of 1.3).
  • Assuming an accrual rate of 15 patients per month
    with a minimum follow up of 6 months would
    require 46.1 months of accrual or 692 patients
  • Median PFS about 3 months
  • Detect hazard ratio of 1.5 in PFS in phase II
    analysis with 90 power using 1-sided .1
    significance

12
Integrated phase II/III study design
  • Patients will be accrued until time t1. At t1
    accrual will be suspended and patients will be
    followed for a minimum time f1.
  • After t1f1 a comparison of the treated versus
    control groups based on progression-free survival
    (PFS) will be performed. If the p-value for PFS
    in this interim analysis is not less than a
    specified threshold a1, accrual will terminate
    and no claims for the new treatment will be made.
  • Otherwise, accrual will resume until a total of M
    patients are accrued. After accruing M patients,
    follow-up will continue for an additional minimum
    time fo. At the end of the study OS will be
    evaluated on all M patients. The total sample
    size M is that of the phase III study.

13
  • For the integrated phase II/III and for the phase
    III with a futility analysis we determined t1 and
    ?1 so that the overall study power (probability
    of concluding a benefit on OS when starting from
    phase II) will be maintained at 81.
  • This 81 is the power for the strategy of a
    randomized phase II study with 90 power for PFS
    followed by a randomized phase III study with 90
    power for OS.
  • For the integrated phase II/III and the futility
    design we evaluate EN and ET for different ?1
    values but always adjusted t1 to maintain 81
    power.

14
  • We evaluated the designs under
  • No treatment effect on either PFS or OS (global
    null)
  • Treatment effect on PFS and OS (global
    alternative)
  • This approach assumes that PFS is a partial
    surrogate for OS i.e. effect of treatment on
    PFS in necessary but not sufficient to ensure
    effect of treament on OS
  • This approach can be used with molecular or
    imaging intermediate endpoint biomarkers instead
    of PFS

15
  • For the single arm phase II study,
    miss-specifying the control median PFS time is a
    serious problem
  • When there is no treatment benefit, Table 1a
    shows the increase in the probability of
    proceeding to phase III if the patients selected
    for the phase II trial are slightly more
    favorable than expected e.g.l median control PFS
    is under specified by 2 weeks and 1 month.

16
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17
  • Table 1b shows that specifying the control median
    too high cuts into the probability of concluding
    a benefit on OS when a benefit exists. The
    overall probability is expected to be .81 but it
    is reduced to .51 or .09 for a 2 week or 1 month
    over specification.

18
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19
  • Although the single arm phase II study may appear
    to speed up drug development, even minimal
    prognostic bias in comparison to historical
    controls can have major impact on producing
    misleading results which either lead to futile
    phase III trials or result in missing active
    agents.

20
  • Dixon, DO, and Simon, R. Sample size
    considerations for studies comparing survival
    curves using historical controls. J. Clin.
    Epidemiology 41 1209-1214, 1988.
  • Thall, PF, and Simon, R. Incorporating
    historical control data in planning phase II
    clinical trials. Stat. in Med. 9215-228, 1990.
  • Thall, P F and Simon R. A Bayesian approach to
    establishing sample size and monitoring criteria
    for phase II clinical trials. Controlled
    Clinical Trials 15463-481, 1994.
  • Thall, PF, Simon R. and Estey E. Bayesian
    designs for Clinical trials with multiple
    outcomes.Statistics in Medicine 14357-379, 1995
  • Thall PF, Simon R, Estey E A new statistical
    strategy for monitoring safety and efficacy in
    single-arm clinical trials. Journal of Clinical
    Oncology 14296-303, 1996.

21
Number of Patients on Experimental Treatment to
have 80 Power for Detecting 15 Absolute
Increase (?.05) in PFS vs Historical Controls
22
  • Table 2 gives the ET and EN for the designs
    under the global null and global alternative. All
    designs have 81 power and type I error rate of
    less than .05 (2-sided).
  • Under the global null hypothesis,
  • The sample size for the integrated design is
    comparable to that for a separate randomized
    phase II design.
  • For the integrated design, futility monitoring on
    PFS is more effective than futility monitoring on
    OS because progression events can be observed
    sooner.
  • Under the global alternative, there is a dramatic
    savings in time and patients for the integrated
    design compared to the sequence of studies.

23
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24
  • The interim analysis of PFS may support a claim
    of accelerated approval if a significance level
    no greater than .05 is used.
  • This design would ensure that a randomized phase
    III trial based on OS was in place at the time
    that accelerated approval was obtained and would
    provide a well powered, well designed randomized
    phase II study with PFS as the basis for the
    provisional claim.

25
  • We have provided a web based computer program
    that calculates the expected sample size,
    expected study duration, and power for the
    integrated phase II/III design and the
    alternatives compared
  • http//brb.nci.nih.gov

26
Using Genomic Predictive Biomarkers in Phase III
Clinical Trials
27
Prognostic Predictive Biomarkers
  • Most cancer treatments benefit only a minority of
    patients to whom they are administered
  • Being able to predict which patients are likely
    to benefit would
  • Save patients from unnecessary toxicity, and
    enhance their chance of receiving a drug that
    helps them
  • Control medical costs
  • Improve the success rate of clinical drug
    development

28
  • Predictive biomarker
  • Measured before treatment to identify who is or
    is not likely to benefit from a particular
    treatment
  • ER, HER2, KRAS
  • Index or classifier that summarizes expression
    levels of multiple genes

29
Predictive Biomarkers
  • In the past often studied as exploratory post-hoc
    subset analyses of RCTs.
  • Led to conventional wisdom
  • Only hypothesis generation
  • Only valid if overall treatment difference is
    significant

30
Drug Development With Companion Diagnostic
  • Develop a completely specified genomic classifier
    of the patients likely to benefit from a new drug
  • Establish analytical validity of the classifier
  • Use the completely specified classifier to design
    and analyze a new clinical trial to evaluate
    effectiveness of the new treatment with a
    pre-defined analysis plan that preserves the
    overall type-I error of the study.

31
Guiding Principle
  • The data used to develop the classifier must be
    distinct from the data used to test hypotheses
    about treatment effect in subsets determined by
    the classifier
  • Developmental studies are exploratory
  • Studies on which treatment effectiveness claims
    are to be based should be definitive studies that
    test a treatment hypothesis in a patient
    population completely pre-specified by the
    classifier

32
Enrichment Design
  • Restrict entry to the phase III trial based on
    the binary predictive classifier, i.e. targeted
    design

33
Develop Predictor of Response to New Drug
Using phase II data, develop predictor of
response to new drug
Patient Predicted Responsive
Patient Predicted Non-Responsive
Off Study
New Drug
Control
34
Applicability of Enrichment Design
  • Primarily for settings where the classifier is
    based on a single gene whose protein product is
    the target of the drug
  • eg trastuzumab
  • Analytical validation, biological rationale and
    phase II data provide basis for regulatory
    approval of the test
  • Phase III study focused on test patients to
    provide data for approving the drug

35
Evaluating the Efficiency of Enrichment Design
  • Simon R and Maitnourim A. Evaluating the
    efficiency of targeted designs for randomized
    clinical trials. Clinical Cancer Research
    106759-63, 2004 Correction and supplement
    123229, 2006
  • Maitnourim A and Simon R. On the efficiency of
    targeted clinical trials. Statistics in Medicine
    24329-339, 2005.
  • reprints and interactive sample size calculations
    at http//linus.nci.nih.gov

36
Stratification Design
37
  • Do not use the diagnostic to restrict
    eligibility, but to structure a prospective
    analysis plan
  • Having a prospective analysis plan is essential
  • Stratifying (balancing) the randomization is
    useful to ensure that all randomized patients
    have tissue available but is not a substitute for
    a prospective analysis plan
  • The purpose of the study is to evaluate the new
    treatment overall and for the pre-defined
    subsets not to modify or refine the classifier
  • The purpose is not to demonstrate that repeating
    the classifier development process on independent
    data results in the same classifier

38
  • R Simon. Using genomics in clinical trial design,
    Clinical Cancer Research 145984-93, 2008

39
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40
Analysis Plan A(substantiall confidence in test)
  • Compare the new drug to the control for
    classifier positive patients
  • If p0.05 make no claim of effectiveness
  • If p? 0.05 claim effectiveness for the
    classifier positive patients and
  • Compare new drug to control for classifier
    negative patients using 0.05 threshold of
    significance

41
Analysis Plan B(Limited confidence in test)
  • Compare the new drug to the control overall for
    all patients ignoring the classifier.
  • If poverall? 0.03 claim effectiveness for the
    eligible population as a whole
  • Otherwise perform a single subset analysis
    evaluating the new drug in the classifier
    patients
  • If psubset? 0.02 claim effectiveness for the
    classifier patients.

42
Analysis Plan C(adaptive)
  • Test for difference (interaction) between
    treatment effect in test positive patients and
    treatment effect in test negative patients
  • If interaction is significant at level ?int then
    compare treatments separately for test positive
    patients and test negative patients
  • Otherwise, compare treatments overall

43
Biomarker Adaptive Threshold Design
  • Wenyu Jiang, Boris Freidlin Richard Simon
  • JNCI 991036-43, 2007

44
Biomarker Adaptive Threshold Design
  • Randomized trial of T vs C
  • Have identified a biomarker score B thought to be
    predictive of patients likely to benefit from T
    relative to C
  • Eligibility not restricted by biomarker
  • No threshold for biomarker determined

45
  • Test T vs C restricted to patients with biomarker
    B b
  • Let S(b) be log likelihood ratio statistic
  • Repeat for all values of b
  • Let S maxS(b)
  • Compute null distribution of S by permuting
    treatment labels
  • If the data value of S is significant at 0.05
    level, then claim effectiveness of T for a
    patient subset
  • Compute point and bootstrap interval estimates of
    the threshold b

46
Generalization of Biomarker Adaptive Threshold
Design
  • Have identified K candidate predictive biomarker
    classifiers B1 , , BK thought to be predictive
    of patients likely to benefit from T relative to
    C
  • Eligibility not restricted by candidate
    classifiers

47
  • Test T vs C restricted to patients positive for
    Bk
  • Let S(Bk) be log likelihood ratio statistic for
    treatment effect in patients positive for Bk
  • Do this for each k1,,K
  • Let S maxS(Bk) , k argmaxS(Bk)
  • Compute null distribution of S by permuting
    treatment labels
  • If the data value of S is significant at 0.05
    level, then claim effectiveness of T for patients
    positive for Bk

48
Adaptive Signature Design
  • Boris Freidlin and Richard Simon
  • Clinical Cancer Research 117872-8, 2005

49
Adaptive Signature DesignEnd of Trial Analysis
  • Compare E to C for all patients at significance
    level 0.04
  • If overall H0 is rejected, then claim
    effectiveness of E for eligible patients
  • Otherwise

50
  • Otherwise
  • Using only the first half of patients accrued
    during the trial, develop a binary classifier
    that predicts the subset of patients most likely
    to benefit from the new treatment T compared to
    control C
  • Compare T to C for patients accrued in second
    stage who are predicted responsive to T based on
    classifier
  • Perform test at significance level 0.01
  • If H0 is rejected, claim effectiveness of T for
    subset defined by classifier

51
Treatment effect restricted to subset.10 of
patients sensitive, 10 sensitivity genes, 10,000
genes, 400 patients.
52
Generalization of Biomarker Adaptive Signature
Design
  • Have identified K candidate predictive biomarker
    classifiers B1 , , BK thought to be predictive
    of patients likely to benefit from T relative to
    C
  • Eligibility not restricted by candidate
    classifiers
  • Using a proportion of patients accrued during the
    trial, evaluate the candidate classifiers
  • Select a single candidate classifier B to use
    as part of the primary analysis plan in the final
    analysis. In the final analysis of the subset of
    B positive patients, omit those used for the
    evaluation of the candidate biomarkers

53
Conclusions
  • New biotechnology and knowledge of tumor biology
    provide important opportunities to improve the
    development and utilization of cancer drugs
  • Treatment of broad populations with regimens that
    do not benefit most patients is increasingly no
    longer necessary nor economically sustainable
  • The established molecular heterogeneity of human
    diseases increases the complexity of drug
    development and requires the use of dramatically
    new approaches to the development and evaluation
    of therapeutics

54
Acknowledgements
  • Sally Hunsberger
  • Boris Freidlin
  • Yingdong Zhao
  • Aboubakar Maitournam
  • Wenyu Jiang
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