Selected Issues in Oncology Trial Design

1
Selected Issues in Oncology Trial Design

• Grant Williams, M.D.
• DODP, CDER, FDA

2
Outline of Presentation

• Challenges in oncology trial design
• Non-inferiority trials in oncology
• Time to Progression (TTP)
• The TTP question in a regulatory framework
• TTP-like endpoints
• Pros and Cons of TTP

3
Blinding Oncology Trials

• Problems
• Unmasking of blind by side-effects
• Need to adjust doses
• Opportunities
• Oral drugs with fewer side-effects

4
Use of Placebos in Oncology Trials

• Problem
• Placebo-alone control usually not feasible in
  advanced cancer
• Potential use of placebos
• Settings prevention, adjuvant, or early
  disease
• Add-on designs (Drug A plus Drug B versus Drug A
  plus placebo)
• May allow continuation of drug and placebo after
  failure of Drug A (e.g., bisphosphonates)
• practical orPlacebo-alone treatment is uIn
  advanced settings it Often may not be practical
  and/or ethical for cancer patientuse a
  placebo-alone treatment arm

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No Blind or Placebo, Consequences

• Limits choice of clinical-benefit endpoints
• Limits trial designs
• Control must be an active drug
• Superiority design (preferred)
• requires new drug to be more effective
• or use add-on design
• Non-inferiority design
• requires large trials
• Quality of historical data on active control
  limits NI design
• Result It is difficult to approve drugs that are
  similar but less toxic

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The Combination Drug Problem

• Drug approvals, drug labels, and drug marketing
  focus on effects from individual drugs.
• Many oncology regimens are combinations where the
  efficacy contribution of individual drugs may not
  be precisely defined.

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Non-inferiority
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Non-Equivalent Words

• Superiority
• Determined with statistical confidence
• Equivalence
• Has no statistical meaning
• Non-inferiority
• Definition no worse by a specified margin
• Proving non-inferiority does not necessarily
  prove efficacy (next slides)
• Not statistically different
• has no meaning without details

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Regulatory Goal of NI Trial

• Demonstrate Drug B is effective
• By referring to historical Drug A effect
• By randomizing A versus B
• By prospectively identifying a margin that
  includes an acceptable fraction of Drug A
  efficacy
• By proving that Drug B is no worse than Drug A by
  that margin
• By determining that the constancy assumption is
  valid

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Critical Assumption of NI Trial

• Constancy assumption
• The historically observed drug effect of the
  active control drug also exists in the current NI
  trial and population
• Potential differences
• Population
• Supportive care
• Additional available therapies
• Study design (observation frequency, etc.)
• Violating this assumption could lead to approval
  of toxic placebo

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Sloppiness / Poor Quality Data

• Sloppiness obscures differences
• Superiority trial designs obscures efficacy
• For NI trials could lead to false efficacy claim

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Determining the Margin from Historical Cancer
Drug Effects

• Step 1 Estimate effect size and confidence
  intervals of active control drug
• Needed (Ideally)
• Multiple historical trials showing effect
• Consistent large drug effect
• Oncology reality
• Small historical drug effect in one or two trials
• Leads to very small margin
• Leads to very large NI studies
• Drug combinations even more complicated

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The Effectiveness Standard

• 1962 amendments claimed effect
• Subsequent rulings Clinical meaning
• Clinical meaning in oncology
• 1970s minimal activity
• 1985 survival or effect on QOL (symptoms or
  function)
• 1990s-2000s use of some surrogates

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Surrogates in Drug Approval

• Surrogate endpoint definition
• Substitute for a clinically meaningful endpoint
  that measures directly how a patient feels,
  functions or survives.
• Changes are expected to reflect changes in a
  clinically meaningful endpoint.
• Temple RJ, Clinical Measurement in Drug
  Evaluation. Nimmo and Tucker. John Wiley Sons
  Ltd, 1995.

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Established Surrogates Supporting Regular Approval

• Blood pressure
• Blood sugar
• Blood cholesterol

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Oncology Surrogates

• AA surrogate reasonably likely
• Validated Surrogates
• Few and far between
• Surrogates for CB supporting regular approval
• Judged by FDA and experts in the field to be
  reliable indicators of CB

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The Ideal Prentices Sufficient Conditions
The surrogate endpoint must be correlated with
the clinical outcome
The surrogate endpoint must fully capture the net
effect of treatment on the clinical outcome
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Surrogate Endpoint Validation

• Meta-analyses of clinical trials data
• Comprehensive understanding of
• The causal pathways of the disease process
• The interventions intended and unintended
  mechanisms of action

From Tom Fleming, Ph.D.
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Is TTP a Clinical Benefit Measure?

• Does TTP have clinical meaning?
• Cancer growth leads to suffering and death
• Delaying cancer growth is good

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Is TTP a Clinical Benefit Measure?

• The critical issues
• Can you measure TTP reliably?
• How much progression delay is worth how much
  toxicity?
• What is the relative meaning of a TTP benefit to
  other benefits such as survival?

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Acceptance of Clinical Benefit Based on Tumor
Effects (RR or TTP), Examples

• Hormonal drugs for metastatic breast cancer
• Primary endpoint response rate (RR)
• Secondary endpoints TTP and Survival
• Regulatory acceptance
• long experience with tamoxifen
• no proven survival benefit for drugs in this
  setting
• low drug toxicity

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TTP and Cytotoxic Drugs for First-line Treatment
of Metastatic Breast Cancer (ODAC, 1999)

• Determination
• Not for full approval
• Yes for Accelerated Approval
• Acceptable effect size not stated
• Deliberations
• Possible survival benefit from chemotherapy?
• Only small TTP benefits with current drugs
• Poor correlation with survival?
• Unreliable TTP measurements?
• Reliability requires frequent measurement?

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What is TTP?

• Complex Check the protocol,case report form,
  statistical analysis plan!
• Time from randomization to first evidence of
  progression. RECIST
• 20 increase in sum of marker lesions
• New lesions
• Unequivocal increase in non-marker lesions

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Which Events Count?Time to Tumor Progression
(TTP)

• TTP event progression
• Measures tumor effects
• Deaths are censored at last visit
• Non-informative censoring assumption

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Which Events Count?Progression Free Survival
(PFS)

• PFS events progression death
• Better surrogate for CB?
• Poor follow-up causes prolongation of
  progression time
• Need careful follow-up
• Need analysis rules for deaths after loss to
  follow-up?

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Which Events Count?Time to Treatment Failure
(TTF)

• TTF events death, progression, toxicity, etc.
• Does not isolate efficacy
• Not adequate as the primary regulatory endpoint
• Drug must be safe and effective
• Demonstrating less toxicity is not adequate

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TTP Advantages

• Measured in all patients
• Measures cytostatic activity
• Oncologists usually change therapy at progression
• Assessed before crossover
• Requires smaller studies
• Face validity?

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TTP Problems

• Doesnt always correlate with survival
• (vs. inadequate data to assess relationship?)
• Indirect measure of patient benefit
• Unclear meaning of small difference
• Reliability in unblinded setting?
• Unknown reliability of small TTP difference with
  usual trial monitoring
• Expensive to measure, difficult to verify

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The Relationship between TTP and Survival

• Data are usually inadequate to assess
• Many different cancer settings
• Large survival benefits are rare
• Cited lack of correlation usually invalid
• Greater statistical power for TTP than survival
• Studies cannot rule out survival effect
• Significant TTP analysis and non-significant
  survival analysis would be expected
• Crossover may obscure survival effect

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Survival versus TTP
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Problem 2TTP is Indirect measure of benefit

• TTP would be more persuasive benefit measure
  when
• When symptoms frequently occur at or soon after
  progression time
• When TTP increment is large
• When treatment toxicity is low
• When benefit of available drugs is less

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Incorporate symptoms into TTP time to
symptomatic progression

• Represents full clinical benefit
• Potential bias in symptom data
• Symptom data needed beyond tumor progression time
• Confounding effects of additional treatments

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Determining Event Dates
Survival Analysis
Survival Event Date
Visit 1
Visit 2
Randomization
TTP Analysis
TTP Event Date
Visit 1
Visit 2
Randomization
Date of Death or actual tumor progression
34
Verifying TTP Difficulties for Sponsors and for
FDA

• What if
• Not all lesions are followed?
• Measurements occur at non-standard times?
• Some measurements are missing from a visit?
• How do you
• Assure equal screening for new lesions?
• Evaluate bias from lack of blinding?
• Verify progression of evaluable disease?

35
Endpoint for Future Research Single Time
Progression Analysis

• Specify analysis point (e.g., 6 months)
• Requires only two data collections
• Document baseline data
• Document either
• Progression before time point
• Stable disease at time point

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Single Time Progression Analysis

• Advantages
• Less data collection
• Minimize time-related bias
• Research questions
• Potential loss of statistical power
• Uncertainty of predicting optimal ST
• Potential for losing information in TTP curve
• Different early effects
• Benefit in curve plateau

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TTP Issues for Consideration

• TTP as a drug approval endpoint?
• Factors determining acceptable settings?
• Amount of evidence needed for TTP claim (
  trials, p value, effect size)

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TTP Issues for Consideration

• Can we improve our approach?
• Research on novel progression endpoints?
• Research on validating TTP?
• Standard approach to endpoint definition and
  censoring methods?
• Blinding investigators and patients?
• Blinded review?
• Including symptoms in endpoint?