Richard Simon, D.Sc R Simon Consulting rsimon Noah Simon, Ph.D.

1.  Adaptive Enrichment Designs for Confirmatory Clinical Trials Specifying the Intended Use Population and Estimating the Treatment Effect Richard Simon, D.Sc R Simon Consulting http://rsimon.us Noah Simon, Ph.D. Department of Biostatistics University of Washington

2. Changed Strategies for Drug Development and Clinical Trial Design • Inter-patient variability of cancers of the same histologic type • Different oncogenic mutations that drive them • Different biologic behavior and response to treatment • Most new cancer treatments are very expensive & only work for a subset of patients with the disease

3. Need to Modify Standard Paradigm of Phase III Clinical Trials • Broad eligibility • Focus on ITT analysis

4. Standard Paradigm May Lead to • False negative studies • Positive studies with: • Large NNT • Small average treatment effects • High economic costs to society

5. Principle of New Paradigm • A phase III trial needs a primary analysis plan that preserves the study-wise type I error level, but that analysis need not be comparison of treatments for the all-randomized population

6. When The Drug Inhibits a Mutated Oncogene • The (targeted) enrichment design is the design of choice

7. Develop Predictor of Benefit from 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 Enrichment Design En Enrichment design

8. Successful use of targeted enrichment design • Trastuzumab, pertuzumab, ado-trastuzumab emtansine for HER2 over-expressed or amplified breast cancer • Vemurafinib, dabrafinib, trametinib for BRAF mutated melanoma • Crizotinib and ceritinib in ALK translocated NSCLC • Afatinib in EGFR mutated NSCLC

9. Advantages of enrichment design • Targets larger treatment effect less diluted by non-sensitive tumors • Often requires many fewer randomized patients than standard designs • Avoids exposing patients to adverse effects of drug until drug is shown effective for those whom it is supposed to benefit • Clarity of interpretation

10. Cancer biology is complex and it is not always possible to have the right single predictive classifier identified with an appropriate cut-point by the time the phase III trial of a new drug is ready to start accrual

14. “Adaptive keep your job design”

15. Candidate Predictive Biomarkers for PD-1 T Cell Checkpoint Therapy • Percentage of tumor cells expressing PD-L1 ligand • Presentation of immunogenic neo-antigens by tumor cells

16. Adaptive enrichment involves restricting the eligibility criteria during the course of the trial based on interim results • Pre-specified interim analysis times • Pre-specified adaptive enrichment algorithm • The Simon & Simon framework includes cut-point based enrichment, single binary marker enrichment or enrichment based on multi-marker modeling

17. Example

18. Example • At interim analysis determine the mle’s of the regression coefficients and their estimated covariance matrix • Compute approximate mean and variance of

20. One statistical significance test is performed at the end of the trial. • It includes all randomized patients

21. Tests strong null that the treatments are equivalent for all patients • Fixed sample size regardless of changes in eligibility • Except for early termination for futility • Significance test preserves the study-wise type I error even with time dependent and data dependent changes to covariate distributions

22. Survival Data • Use intermediate endpoint for adapting eligibility • PFS or response • zk=standardized log-rank statistic for patients accrued in period k but evaluated at final analysis time

23. Our simulations show that adaptive enrichment can substantially increase the statistical power with adaptive threshold determination and multi-biomarker modeling

24. p0=.2, p1=.5, K=5, Ntot=200, all pts 100/yr Single quantitative biomarker uniform on (0,1) Two period cut-point enrichment Binary response; control p0 Response rate for E is p1 if B≥cut-point

26. Example • Specify prior distributions for the regression coefficients • At interim analysis determine the posterior distributions • Exclude eligibility for patients with covariates such that

36. Description of Bootstrap De-biasing for a Two-period clinical trial • Outcome model as function of covariates is estimated using full dataset. • Bootstrap sample of outcomes for first period patients is generated from estimated outcome model • Eligibility restriction is determined for second period • Treatment effect in first period for patients eligible for second period is empirically determined • It is compared to treatment effect for those patients based on the model used to generate the data; the difference is an estimate of bias • Estimates of bias are averaged over multiple bootstrap samples

37. Simulation of J Disjoint Strata with Binary Response • True response probabilities for different strata are not modeled. They are estimated separately from the data. • pT(j)=response probability of test rx for stratum j • pC(j)=response probability of control for stratum j

38. Simulation of J Disjoint Strata with Binary Response • Restrict 2nd period eligibility to stratum j if the likelihood of the stratum j period 1 data for the model pT(j)=pC(j)+δ* relative to the model pT(j)=pC(j) is < a threshold ϒ where δ* and ϒ are pre-specified.

39. 100 pts/period; nsim=1000,nboot=100 Half of strata with rx effect (p0=.2,p1=.5) δ*=γ=.25

43. Thank you