1 / 20

How much can we adapt? An EORTC perspective

How much can we adapt? An EORTC perspective. Saskia Litière EORTC - Biostatistician. I have no conflicts of interest. Outline. Adaptive designs What? Why? The challenges Examples Currently part of EORTC portfolio Currently not (yet) part of EORTC portfolio Take home messages.

brandy
Télécharger la présentation

How much can we adapt? An EORTC perspective

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. How much can we adapt?An EORTC perspective SaskiaLitière EORTC - Biostatistician

  2. I have no conflicts of interest

  3. Outline Adaptive designs • What? • Why? • The challenges • Examples • Currently part of EORTC portfolio • Currently not (yet) part of EORTC portfolio • Take home messages

  4. What is an adaptive design? “… a study that includes a prospectively planned opportunity for modification of one or more specified aspects of the study design and hypotheses based on analysis of data (usually interim data) from subjects in the study. “

  5. Why use adaptive designs? • They aim to make efficient use of patient and financial resources • Allow for real-time learning during the course of a trial • Relatively flexible: modifications possible in the course of trial which make the approach more robust to failure • The drug development process is streamlined and optimized

  6. The challenges • To control the operating characteristics • To control the bias due to the adaptation • Statistical • Operational • To guarantee that the results can be interpreted and explained!

  7. Several possible approaches Well-known • Early stopping for futility and/or efficacy • Drop treatment arm(s) – also known as pick the winner designs • Biomarker adaptive designs • Sample size re-estimation • Adaptive randomization… To name but a few … Less understood

  8. Most of them come down to One trial Change H0? Change design parameters?

  9. A few examples

  10. EORTC 62012 in first line treatment of advanced, high grade STS Interim 1: PFS? Interim 2: OS? Final: OS? Doxorubicin R Group sequential design Doxorubicin + Ifosfamide

  11. TRUSTS (EORTC 62091) in advanced or metastatic STS Phase IIb 3 x 40 pts Phase III 2 x 110 pts Doxorubicin 75 mg/m2 Selectthebest PFS PFS? Doxo 75 mg/m2 R Trabectedin 1.3 mg/m2 3-h T 3-h or 24-h Trabectedin 1.5 mg/m2 24-h Seamless phase II/III design

  12. TRUSTS (EORTC 62091) in advanced or metastatic STS • Both steps are conducted independently and the results of both steps are combined in the end in an overall test result • Shortens time and patient exposure • Relatively flexible • Efficient use of patient resources • Complexdesign: statistics are difficult to explain • Gap in accrual between phase II and phase III • Logistically challenging • Difficult in studies with long-term endpoints • Unless in combination with a short-term endpoint for the phase II part … another long and complex story on type I error and correlation

  13. Sample size re-estimation 2-sided a = 5% Power = 90% HR = 0.7 Cytel Webinar for East®SurvAdapt, October 28, 2010

  14. Sample size re-estimation • May increase the risk of running an enlarged negative trial • Possibility of second guessing • A resampling decision can be easily interpreted as “the treatment is not as efficient as expected” → Operational bias? Accrual? → May require extensive (expensive) logistics Protection of study integrity is essential!

  15. Battle Trial – Adaptive randomization Lee et al. Zhou et al. CT 2008

  16. Battle Trial – Adaptive randomization Probabilities of treatment success updated based on observed results Prior probability of each treatment success given marker Randomizeusing the weights given by prior prob 8-week outcome observed Maximizes the chance that the patient receives the treatment that is most effective for him/her

  17. Adaptive randomization • Sample size? • Requires fast dataflow – logistically demanding especially in large multicenter trials • Does not work for long-term endpoint. • Difficult to interpret results beyond estimation • Comparisons? • Precision? • Recruitment patterns can change during the course of the trial because of deduced knowledge of randomization probabilities

  18. Adaptive randomization • Simulations suggest very similar operational characteristics may be achieved if applying classical 2-stage designs with stopping rules • Korn and Freidlin, JCO 2011 • Yuan and Yin, JCO 2011 • Example of such an alternative: CREATE (EORTC 90101) • A Simon 2-stage design is being used to assess the activity of Crizotinib in each of 6 cohorts of patients (ALK/MET+)

  19. Conclusion • The STBSG EORTC is more adaptive than you may have thought • There are challenging times ahead, both for clinicians as well as statisticians • Flexible design strategies • More efficient use of resources • While the sky seems to be the limit, experience teaches us to be wary and critical of solutions presented as ‘miracles’.

  20. Acknowledgment Stats colleagues at the EORTC, specifically Laurence Collette Jan Bogaerts Murielle Mauer

More Related