Adaptive Designs for Using Predictive Biomarkers in Phase III Clinical Trials - PowerPoint PPT Presentation

About This Presentation
Title:

Adaptive Designs for Using Predictive Biomarkers in Phase III Clinical Trials

Description:

A measurement made before and after treatment to determine whether the treatment is working ... correlating gene expression to patient outcome after treatment ... – PowerPoint PPT presentation

Number of Views:113
Avg rating:3.0/5.0
Slides: 44
Provided by: Richar7
Learn more at: http://linus.nci.nih.gov
Category:

less

Transcript and Presenter's Notes

Title: Adaptive Designs for Using Predictive Biomarkers in Phase III Clinical Trials


1
Adaptive Designs for Using Predictive Biomarkers
in Phase III Clinical Trials
  • Richard Simon, D.Sc.
  • Chief, Biometric Research Branch
  • National Cancer Institute
  • http//linus.nci.nih.gov/brb

2
Biomarkers
  • Surrogate endpoints
  • A measurement made before and after treatment to
    determine whether the treatment is working
  • Surrogate for clinical benefit
  • Predictive classifiers
  • A measurement made before treatment to select
    good patient candidates for the treatment

3
Surrogate Endpoints
  • It is extremely difficult to properly validate a
    biomarker as a surrogate for clinical outcome. It
    requires a series of randomized trials with both
    the candidate biomarker and clinical outcome
    measured

4
Biomarkers as Endpoints in Phase I and II Trials
  • Biomarkers used as endpoints in phase I or phase
    II studies need not be validated as surrogates
    for clinical outcome
  • The purposes of phase I and phase II trials are
    to determine whether to perform a phase III
    trial, and if so, with what dose, schedule,
    regimen and on what population of patients
  • Claims of treatment effectiveness should be based
    on phase III results

5
Unvalidated Surrogate Endpoints in Seamless Phase
II/III Trials
  • Randomized comparison of standard treatment new
    drug
  • Size trial using phase III (e.g. survival)
    endpoint
  • Perform interim futility analysis using phase II
    endpoint (e.g.biomarker or PFS)
  • If treatment vs control results are not
    significant for phase II endpoint, terminate
    accrual and do not claim any benefit of new
    treatment
  • If results are significant for intermediate
    endpoint, continue accrual and follow-up and do
    analysis of phase III endpoint at end of trial
  • Interim analysis does not consume any of the
    significance level for the trial

6
Adaptive Phase I and II Trials
  • Adaptiveness in phase I and II trials can help
    optimize the dose/schedule and patient population
    in order to develop the right pivotal trial
  • Bayesian methods provide great flexibility for
    phase I and II trials
  • Subjective prior distributions can be appropriate

7
Adaptive Methods
  • Have been around for a long time
  • PF Thall, R Simon, SS Ellenberg, (1989) A
    two-stage design for choosing among several
    experimental treatments and a control in clinical
    trials, Biometrika 75303-310.

8
Adaptive Methods
  • Frequentist methods can be very adaptive
  • Sample size
  • Target population
  • Treatment arms
  • The algorithm for adapting should be specified in
    the protocol
  • In assessing statistical significance (or
    confidence intervals) the analysis should take
    into account the adaptiveness algorithm used
  • The rejection region should be calibrated to
    limit the experiment-wise type I error
    (probability of making any false positive claim
    from a study) to 5, taking into account the
    adaptiveness algorithm used

9
Adaptive Methods
  • With Bayesian methods all prior distributions
    must be specified in advance
  • Bayesian inference usually does not control type
    I error
  • Bayesian methods can control the type I error in
    adaptive trials if the algorithm for adaptiveness
    is specified in advance
  • Bayesian methods are often problematic for phase
    III trials because there may be no prior
    distribution appropriate for all parties.

10
Predictive Biomarker Classifiers
  • Many cancer treatments benefit only a small
    proportion of the patients to which they are
    administered
  • Targeting treatment to the right patients can
    greatly improve the therapeutic ratio of benefit
    to adverse effects
  • Treated patients benefit
  • Treatment more cost-effective for society

11
Developmental Strategy (I)
  • Develop a diagnostic classifier that identifies
    the patients likely to benefit from the new drug
  • Develop a reproducible assay for the classifier
  • Use the diagnostic to restrict eligibility to a
    prospectively planned evaluation of the new drug
  • Demonstrate that the new drug is effective in the
    prospectively defined set of patients determined
    by the diagnostic

12
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
13
Applicability of Design I
  • Primarily for settings where the classifier is
    based on a single gene whose protein product is
    the target of the drug
  • With substantial biological basis for the
    classifier, it will often be unacceptable
    ethically to expose classifier negative patients
    to the new drug
  • It is inappropriate to expose classifier negative
    patients to a treatment for the purpose of
    showing that a treatment not expected to work for
    them actually doesnt.

14
Evaluating the Efficiency of Strategy (I)
  • Simon R and Maitnourim A. Evaluating the
    efficiency of targeted designs for randomized
    clinical trials. Clinical Cancer Research
    106759-63, 2004 Correction 123229,2006
  • Maitnourim A and Simon R. On the efficiency of
    targeted clinical trials. Statistics in Medicine
    24329-339, 2005.

15
  • Relative efficiency depends on proportion of
    patients test positive, and effectiveness of drug
    (compared to control) for test negative patients
  • When less than half of patients are test negative
    and the drug has little or no benefit for test
    negative patients, the targeted design requires
    dramatically fewer randomized patients.
  • May require fewer or more patients to be screened
    than randomized with untargeted design

16
Web Based Software for Comparing Sample Size
Requirements
  • http//linus.nci.nih.gov/brb/

17
Developmental Strategy (II)
18
Developmental Strategy (II)
  • Do not use the diagnostic to restrict
    eligibility, but to structure a prospective
    analysis plan.
  • Compare the new drug to the control overall for
    all patients ignoring the classifier.
  • If poverall? 0.04 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? 0.01 claim effectiveness for the
    classifier patients.

19
  • This analysis strategy is designed to not
    penalize sponsors for having developed a
    classifier
  • It provides sponsors with an incentive to develop
    genomic classifiers

20
Predictive Medicine not Correlative Science
  • The purpose of the RCT is to evaluate the new
    treatment overall and for the pre-defined subset
  • The purpose is not to re-evaluate the components
    of the classifier, or 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

21
Developmental Strategy III
  • Do not use the diagnostic to restrict
    eligibility, but to structure a prospective
    analysis plan.
  • Compare the new drug to the control for
    classifier positive patients
  • If pgt0.05 make no claim of effectiveness
  • If p? 0.05 claim effectiveness for the
    classifier positive patients and
  • Continue accrual of classifier negative patients
    and eventually test treatment effect at 0.05
    level

22
Separate testing of treatment effect in positive
and negative subsets
  • If the drug is expected to be effective overall,
    design III will not be attractive as it requires
    commitment to a double sized clinical trial
  • The chance of a false negative in at least one
    subset is 19
  • the potential value of being able to do a subset
    analysis may not be worth the cost of having to
    demonstrate effectiveness in both subsets
    separately for broad labeling

23
The Roadmap
  1. Develop a completely specified genomic classifier
    of the patients likely to benefit from a new drug
  2. Establish reproducibility of measurement of the
    classifier
  3. 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.

24
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
  • And not closely regulated by FDA
  • FDA should not regulate classifier development
  • 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

25
Retrospective-Prospective Study
  • Use archived samples from a non-targeted
    (negative) clinical trial to develop a binary
    classifier of a subset thought to benefit from
    treatment
  • Develop a single binary classifier
  • Develop an assay for that classifier and
    establish analytical validity of that assay
  • Write a protocol for testing effectiveness of the
    new treatment compared to control in classifier
    positive patients in a separate randomized
    clinical trial
  • New targeted type (I) trial
  • Using archived specimens from a second previously
    conducted randomized clinical trial

26
Development of Genomic Classifiers
  • Single gene or protein based on knowledge of
    therapeutic target
  • Single gene or protein based on evaluation of a
    set of candidate genes or assays
  • Empirically determined based on genome-wide
    correlating gene expression to patient outcome
    after treatment

27
Development of Genomic Classifiers
  • During phase II development or
  • After failed phase III trial using archived
    specimens.
  • Adaptively during early portion of phase III
    trial.

28
Adaptive Signature Design An adaptive design for
generating and prospectively testing a gene
expression signature for sensitive patients
  • Boris Freidlin and Richard Simon
  • Clinical Cancer Research 117872-8, 2005

29
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

30
  • Otherwise
  • Using specimens from patients accrued during the
    first half of the trial, develop a binary
    classifier that predicts the subset of patients
    most likely to benefit from the new treatment E
    compared to control C
  • Compare E to C for patients accrued in second
    stage who are predicted responsive to E based on
    classifier
  • Perform test at significance level 0.01
  • If H0 is rejected, claim effectiveness of E for
    subset defined by classifier

31
Treatment effect restricted to subset.10 of
patients sensitive, 10 sensitivity genes, 10,000
genes, 400 patients.
Test Power
Overall .05 level test 46.7
Overall .04 level test 43.1
Sensitive subset .01 level test (performed only when overall .04 level test is negative) 42.2
Overall adaptive signature design 85.3
32
Overall treatment effect, no subset
effect.10,000 genes, 400 patients.
Test Power
Overall .05 level test 74.2
Overall .04 level test 70.9
Sensitive subset .01 level test 1.0
Overall adaptive signature design 70.9
33
Possible Modifications to Adaptive Signature
Design
  • Refine .04/.01 split to take correlation of tests
    into account
  • If overall test is not significant at .04 level,
    develop binary classifier and continue accrual of
    classifier positive patients
  • Application to selecting among a few candidate
    tests in rather than developing gene expression
    signature

34
Biomarker Adaptive Threshold Design
  • Wenyu Jiang, Boris Freidlin Richard Simon
  • JNCI 991036-43, 2007
  • http//linus.nci.nih.gov/brb

35
Biomarker Adaptive Threshold Design
  • Randomized pivotal trial comparing new treatment
    E to control C
  • Survival or DFS endpoint
  • Have identified a univariate biomarker index B
    thought to be predictive of patients likely to
    benefit from E relative to C
  • Eligibility not restricted by biomarker
  • No threshold for biomarker determined
  • Biomarker value scaled to range (0,1)

36
Biomarker Adaptive Threshold Design (A)
  • Compare E vs C overall using significance
    threshold of 0.04
  • If significant, claim broad effectiveness of E
  • If not significant,
  • Compare E vs C for patients with B gt b
  • Do this for each possible threshold value b
  • Compute log likelihood ratio statistic S(b)for
    treatment versus control effectiveness in subset
    with Bgtb
  • Find b that maximizes S(b)
  • Define TS(b)
  • Compute significance of T by permuting patients
    to E and C
  • If significance level, adjusted for optimal
    threshold is lt 0.01, then claim treatment
    effectiveness for subset
  • Compute bootstrap confidence interval for optimal
    threshold b

37
Biomarker Adaptive Threshold Design (A)
  • Compute significance of maximized treatment
    difference by permuting labels of which
    treatments are assigned to which patients
  • Hold fixed the B values for each patient and the
    total number of patients in each treatment group
  • Re-analyze the permuted data, comparing
    treatments for subset with Bgtb for each b,
    computing S(b) and determining threshold b with
    largest S(b) value
  • TS(b) for permuted data
  • Repeat for 10,000 permutations
  • Count the proportion of random permutations that
    result in S(b) values as large as S(b) for the
    real data
  • That is p value adjusted for optimizing over
    threshold values
  • If significance level, adjusted for optimal
    threshold is lt 0.01, then claim treatment
    effectiveness for subset
  • Compute bootstrap confidence interval for optimal
    threshold b

38
Estimated Power of Broad Eligibility Design
(n386 events) vs Adaptive Design (n412
events)80 power for 30 hazard reduction
Model Broad Eligibility Design Biomarker Adaptive Design
40 reduction in 50 of patients (20 overall reduction) .70 .78
60 reduction in 25 of patients (20 overall reduction) .65 .91
79 reduction in 10 of patients (14 overall reduction) .35 .93
39
Procedure B
  • S(b)log likelihood ratio statistic for treatment
    versus control comparison in subset of patients
    with B?b
  • Compute S(b) for all possible threshold values
  • Determine b value for which S(b) is maximum
  • TmaxS(0)R, S(b)
  • Compute null distribution of T by permuting
    treatment labels
  • Permute the labels of which patients are in which
    treatment group holding fixed the number of
    patients in each treatment group and the B value
    for each patient
  • Re-analyze the data
  • Compare treatment to control for each subset of
    patients with B?b for all cutpoints b
  • Compute T for the permuted data
  • Repeat for 10,000 permutations

40
Procedure B
  • If the data value of T is significant at 0.05
    level using the permutation null distribution of
    T, then reject null hypothesis that E is
    ineffective
  • Compute point and interval estimates of the
    threshold b

41
(No Transcript)
42
(No Transcript)
43
(No Transcript)
Write a Comment
User Comments (0)
About PowerShow.com