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Use of Phase II Data

Use of Phase II Data. Phase II Trials - Purpose. To develop hypotheses. Phase II Trials - Purpose. To provide estimates. To develop hypotheses. Phase II Trials - Purpose. To identify. winners and losers in phase III. To provide estimates. To develop hypotheses.

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Use of Phase II Data

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  1. Use of Phase II Data

  2. Phase II Trials - Purpose To develop hypotheses

  3. Phase II Trials - Purpose To provide estimates To develop hypotheses

  4. Phase II Trials - Purpose To identify winners and losers in phase III To provide estimates To develop hypotheses

  5. Phase II Trials – PurposeTargeted Therapies To identify biomarkers To identify winners and losers in phase III To provide estimates To develop hypotheses

  6. Selected Phase II Issues • There is evolving doubt about the efficiency of the single arm phase II trial as a strategy to identify phase III experimental arms • The randomized phase II design is preferred in order to address patient selection issues (the issue is suitability / external validity) • Different phase III designs are used

  7. Randomized Phase II Issues • The design is to establish external validity • Multiple design formats exist • The randomized phase II selection design is to allow “pick the winner” • The randomized phase II comparative design is to more formally identify a signal • There is increasing use of II-III designs

  8. Randomized Comparative Phase II Issues • This design is ‘explanatory’ to the extreme • Patients are highly selected • The maneuver is precisely prescribed • The outcome is often a surrogate, which is carefully scheduled and adjudicated • Statistical parameters may be less stringent

  9. Problems in Using Phase II Data for Policy Decisions • Design does match intent • Suitability / external validity • Uncertain prediction for phase III results • If phase III were a ‘go’, different design aspects would be incorporated

  10. Superiority vs. Non-inferiority

  11. Superiority vs. Non-inferiority A new treatment is: • ‘as good’ at disease control and is: • Less toxic • Associated with a better QoL • More cost effective • More convenient

  12. Superiority vs. Non-inferiority Key Principles: • Include superiority for a 2o outcome • Define the non-inferiority boundary The benchmark will be the upper 95% CI • Be better than ‘putative placebo’ • Include an as-treated analysis Kaul, Ann Int Med 2006

  13. Treatment Differences in Noninferiority Trials Piaggio, JAMA 2006

  14. The Putative Placebo Kaul, Ann Int Med 2006

  15. The Putative Placebo 0 1 2 3 4 5 Death Rate with Standard Death Rate with Placebo

  16. The Putative Placebo Benefit is 2.5% (2.0-3.0%) 0 1 2 3 4 5 Death Rate with Standard Death Rate with Placebo

  17. The Putative Placebo New NI Therapy 0 1 2 3 4 5 Death Rate with Standard Death Rate with Placebo

  18. Principle #1: The NI margin should not cross the old control arm The upper 95% CI boundary must be < +2.5 New NI Therapy 0 1 2 3 4 5 Death Rate with Standard Death Rate with Placebo

  19. Principle #2: The NI margin should include the old control arm interval for maximum benefit The upper 95% CI boundary must be < +2.5 New NI Therapy 0 1 2 3 4 5 Death Rate with Standard Death Rate with Placebo

  20. The maximum parameters mean that the New Therapy point estimate can be no more than 1% worse 0 1 2 3 4 5 Death Rate with Standard Death Rate with Placebo

  21. However, the upper boundary of the 95% CI may be unacceptable according to a new non-inferiority margin 0 1 2 3 4 5 Death Rate with Standard Death Rate with Placebo

  22. However, the upper boundary of the 95% CI may be unacceptable according to a new non-inferiority margin Acceptable Unacceptable 0 1 2 3 4 5 Death Rate with Standard Death Rate with Placebo

  23. The ‘As–treated’ Analysis • For superiority trials, the ITT is conservative and reduces risks of bias • Consider randomization to a pill vs. a transplant, where transplant is experimental • Contamination where transplant is avoided may occur in a non-random manner (healthier patients undergo transplant) • An as-treated analysis would represent bias as the compared groups would have systematic difference in their characteristics • ‘Noise’ is a buffer that goes against hypothesis

  24. The ‘As–treated’ Analysis • For NI trials, the noise of an ITT tends to bring arms together and thus supports confirming the hypothesis • Consider randomization to a pill vs. a transplant, where transplant is standard • Contamination where transplant is provided may occur in a non-random manner (patients with higher-risk disease undergo transplant) • An ITT analysis would potentially misclassify those higher-risk patients as benefiting from the pill • Both analyses should be performed

  25. Superiority vs. Non-inferiority Beware of: • A wolf in sheep’s clothing (superiority trial that fails to meet endpoint) • The lack of a superior 2o outcome

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