1. CI - 1 Cure Rate Models and Adjuvant Trial Design for ECOG Melanoma Studies in the Past, Present, and Future Joseph Ibrahim, PhD Harvard School of Public Health, Dana-Farber Cancer Institute, and ECOG Statistical Center

2. CI - 2 Outline  Rationale for cure rate models in trial design  What is a cure rate model  How to design trials using cure rate models  Statistical designs for E1684, E1690, E1697, and E1694  Noninferiority designs using cure rate models – E1601  Future designs for trials involving HDI

3. CI - 3 Cure Rate Models  The cure rate model is useful for designing studies with time-to-event endpoints, such as RFS and OS  It is most useful when a plateau is reached in the survival curve after sufficient follow-up  For adjuvant melanoma studies, this plateau occurs after approximately 5 yr based on the ECOG experience

4. CI - 4 Plateau for E1684 Relapse-Free Survival Time interval and no. events/no. at risk Group 0-2 2-4 4-6 6-8 8-10 10-12 12-14 14-16 Observation 89/140 12/51 3/39 0/35 1/32 1/29 0/15 0/3 Interferon 73/146 14/68 3/53 1/50 2/48 2/44 0/31 0/10 0 0.2 0.4 0.6 0.8 1.0 0246810121416 Time, yr IFN vs Observation: P 2 =.02, P 1 =.01, HR = 1.38 Proportion alive and relapse free Observation Interferon

5. CI - 5 Plateau for E1684 Overall Survival 0 0.2 0.4 0.6 0.8 1.0 0246810121416 Time, yr Proportion alive IFN vs Observation: P 2 =.18, P 1 =.09, HR = 1.22 Time interval, no. events/no. at risk Group 0-2 2-4 4-6 6-8 8-10 10-12 12-14 14-16 Observation 60/140 22/80 10/57 1/46 2/43 0/38 0/21 0/6 Interferon 54/146 19/90 10/70 3/60 2/56 5/52 0/35 0/10 Observation Interferon

6. CI - 6 Plateau for E1690 Relapse-Free Survival 0 0.2 0.4 0.6 0.8 1.0 0123456710 Time, yr Proportion alive and relapse free IFN vs Observation: P 2 =.09, HR = 1.24 Time interval, no. events/no. at risk Group 0-2 2-4 4-6 6-8 8-10 Observation 105/212 16/94 5/72 2/44 0/13 Interferon 98/215 15/108 5/85 2/53 0/20 Observation Interferon 89

7. CI - 7 Assumptions for Cure Rate Model (1)  The cure rate model assumes that study population consists of 2 subpopulations: cured and not cured   = proportion of patients who are cured  1 –  = proportion of patients who are not cured

8. CI - 8 Assumptions for Cure Rate Model (2)  The proportion of patients not cured experience events according to an exponential distribution with rate  The probability of surviving beyond time t is given by S(t) =  + (1 -  ) exponential (– t)  S(t) is the vertical axis in a Kaplan-Meier plot

9. CI - 9 Properties of Cure Rate Model   =.26 means that 26% of the population are “cured” and 74% are “not cured”  If  = 0, then we obtain an exponential survival model  The cure rate model fits the data better than an exponential model when a plateau occurs in the right tail of the survival curve  For E1684, a cure rate model fit the data better than an exponential model  The log-rank test yields high statistical power when cure rate model is used in design

10. CI - 10 Statistical Design for E1684  E1684: 2-arm study of HDI vs Obs  Survival assumed to follow an exponential model (no prior experience to guide design)  4 yr of accrual, 3 yr of follow-up  Sample size of 285 yields 83% power  Detect 50% improvement in median RFS from 1.5 to 2.25 yr

11. CI - 11 Statistical Design for E1690  E1690: 3-arm study, HDI vs LDI vs Obs  Cure rate model was used in the statistical design based on E1684 experience  4 comparisons of interest –HDI vs Obs with respect to RFS and OS –LDI vs Obs with respect to RFS and OS –A 1-sided significance level of.025 was used for each comparison

12. CI - 12 Design Assumptions for E1690 (1)  Based on the Obs arm of E1684, the estimate of the long-term cure rate (  ) is 26.4% for relapse-free survival and 32.5% for overall survival  The estimate of median survival among noncured patients (log(2)/ ) is 0.576 yr for RFS and 1.312 yr for OS  The estimate of the cure rate for RFS on the HDI arm of E1684 was 37.9% (an improvement of 12% over no therapy)

13. CI - 13 Design Assumptions for E1690 (2)  4.5 yr of accrual, 2.5 yr of follow-up  Sample size of 625 yields –81% power for RFS to detect 10% increase in cure rate 50% increase in median RFS among noncured group –82% power for OS to detect 10% increase in cure rate 50% increase in median OS among noncured group

14. CI - 14 Design Assumptions for E1690 (3) Relapse-free survival  Null hypothesis:  = 26.4%, median RFS time = 6.9 mo  Alternative hypothesis:  = 36.4%, median RFS time = 10.4 mo Overall survival  Null hypothesis:  = 32.5%, median OS time = 15.7 mo  Alternative hypothesis:  = 42.5%, median OS time = 23.6 mo

15. CI - 15 Sequential Monitoring for E1690  Sequential monitoring is used in all phase III ECOG studies  4 interim analyses were planned at times corresponding to equal amounts of statistical information accrued on RFS

16. CI - 16 Sequential Monitoring Plan for E1690 Relapse-free survival HDI vs Obs LDI vs Obs InformationNumber of Nominal Information Number of Nominal time recurrences significance 0.258 65< 10 -5.310 78.0000145 0.517130.00101.517130.00101 0.756190.00772.756190.00772 1.000252.02231.000252.0223 Overall survival HDI vs Obs LDI vs Obs InformationNumber of Nominal Information Number of Nominal time recurrences significance.141 28< 10 -6.151 30 < 10 -6.398 79.000146.623124.00308 1.000199.02391.000199.0239

17. CI - 17 Statistical Design for E1694  E1694: 2-arm study of GMK vs HDI  First melanoma trial design using HDI as control arm  Designed as a superiority trial  Cure rate model used in the statistical design

18. CI - 18 E1694 Design Assumptions (1)  1-sided significance level of.025  Cure rate and median time to event among noncured group for HDI were estimated from E1690 data

19. CI - 19 E1694 Design Assumptions (2)  3.3 yr of accrual, 2 yr of follow-up  Total sample size of 851 patients yields –86% power for the RFS endpoint –80% power for the OS endpoint to detect 10% increase in cure rate 15% relative increase in median time to event among noncured group

20. CI - 20 E1694 Design Assumptions Median time Cure rate, % to event, yr HDI GMK Relapse-free survival 35.9 45.9.747.859 Overall survival 38.4 48.4 1.658 1.907

21. CI - 21 E1694 Sequential Monitoring Plan for RFS Rejection Nominal Real Information Relapses Upper probability significance time, yr time under H 1 bound under H 0 level 2.01 0.35 149 3.6128 <0.0001 <.0001 2.820.612602.65060.004.004 3.870.86 366 2.1894 0.012.014 5.30 1.00 426 2.0536 0.009.020 0.025 H 0 = Null hypotheses. H 1 = Alternative hypothesis.

22. CI - 22 E1694 Sequential Monitoring Plan for OS Rejection Nominal Real Information Deaths Upper probability significance time, yrtime under H 1 bound under H 0 level 2.01 0.25 81 4.3326 < 0.0001 <.0001 2.820.471513.07380.001.001 3.870.74 238 2.3757 0.008.0109 5.30 1.00 322 2.0132 0.016.022 0.025 H 0 = Null hypotheses. H 1 = Alternative hypothesis.

23. CI - 23 Statistical Design for E1697  Patient population (ECOG/US): T3N0 (International NCI-Canada, Australia): T3N0, T4N0, Tany, N1a  First ECOG phase III trial for this patient population  2-arm trial of 1-mo HDI vs Obs  Designed as a superiority trial

24. CI - 24 E1697 Statistical Design Assumptions (1)  Primary endpoints are RFS and OS  Cure rate model is used in the statistical design  1-sided significance level of 0.025

25. CI - 25 E1697 Statistical Design Assumptions (2)  Sample size of 1,420 patients yields  3 yr of accrual, 3 yr of follow-up –88% power for both RFS and OS to detect 7.5% increase in cure rate 15% relative increase in median time to event among noncured group

26. CI - 26 E1697 Statistical Design Assumptions (3) Median time Cure rate, % to event, yr Obs HDI Relapse-free survival 65 72.5 1.5 1.725 Overall survival 75 82.5 2.5 2.875

27. CI - 27 E1697 Sequential Monitoring Plan for RFS Upper bound Nominal Real Information Relapses for earlysignificance time, yr time under H 1 stopping level 1.950.25 854.3326 <.0001 2.920.501692.9680.0030 3.990.752542.3617.0182 5.000.903052.1681.0301 6.001.003392.0774.0377 H 1 = Alternative hypothesis.

28. CI - 28 E1697 Sequential Monitoring Plan for OS Upper bound Nominal Real InformationDeaths for earlysignificance time, yr time under H 1 stopping level 2.15 0.25 46 4.3326 <.0001 3.20 0.50 93 2.9680.0030 4.33 0.75 139 2.3617.0182 5.25 0.90 167 2.1681.0301 6.00 1.00 186 2.0774.0377 H 1 = Alternative hypothesis.

29. CI - 29 Conditional Power Considerations for E1697 (1) Conditional power  Probability of observing a significant result at full information, given the current data and the specified alternative under the statistical design  Conditional power allows us to stop the study early if experimental therapy not better than control  Timing of conditional power calculation is important

30. CI - 30 Conditional Power Considerations for E1697 (2)  In most ECOG studies, we carry out conditional power calculations  Conditional power is very important in trials involving observation arms or trials investigating A vs A + B  Conditional power calculations are to be carried out at each interim analysis of E1697

31. CI - 31 Noninferiority Designs  These designs will play a prominent role in future trials involving HDI  Especially relevant for future trials involving HDI and vaccine  Within the context of the cure rate model, these designs can be constructed by taking small differences in cure rates  Sample size increases dramatically with cure rate differences of  5%  Use a higher significance level than the conventional 0.05 level

32. CI - 32 Statistical Design for E1601 (1)  Patient population: T4N0, TANY, N1a, and N2a §  2-arm, noninferiority trial of 1-mo HDI vs 1-yr HDI  Primary endpoint is RFS §Only one positive node.

33. CI - 33 Statistical Design for E1601 (2)  Will declare 1-mo HDI arm noninferior if –There is < 25% absolute difference in median RFS for those not cured –And < 3% absolute difference in the cure rate between the 2 arms  With a noninferiority design, a high power (at least 90%) is desirable  E1601 designed with 95% power  Power based on 1-sided, log-rank test with significance level of 0.075

34. CI - 34 Statistical Design for E1601 (3)  Assume 4 yr of accrual and 6 yr of follow-up  Sample size of 2,780 yields –95% power for RFS to detect 3% increase in cure rate 25% increase in median time to event in noncured group

35. CI - 35 Statistical Design for E1601 (4) Median time Cure rate, % to event, yr 1-yr HDI 1-mo HDI Relapse-free survival 63 60 0.90 0.65

36. CI - 36 Sequential Monitoring Plan for E1601 (1) Nominal Real Information Relapses Upper significance time, yr time under H 1 bound § level 1.95.25 254 3.3747.0004 3.09.50 507 2.2710.012 4.15.75 761 1.7990.036 7.23.90 913 1.6647.048 10.0 1.00 1016 1.6041.054 §Upper bound for rejecting noninferiority in favor of I year of HDI.

37. CI - 37 Sequential Monitoring Plan for E1601 (2)  Conditional power will be computed to determine the noninferiority of 1 mo of HDI relative to 1 yr of HDI  Conditional power based on RFS endpoint  Conditional power computed at the 75% and 90% information times to allow for sufficient follow-up  Accrual goal is attained at 75% statistical information

38. CI - 38 Sample Sizes Under Several Design Scenarios to Achieve 95% Power Cure rate Difference in median Sample difference, % time to event, % size 3 25 2640 3 20 3300 315 4400 525 1520 520 1820 515 2260 725 1040 720 1220 715 1400 1025 600 1020 680 1015 760

39. CI - 39 Future Trial Designs  Noninferiority designs of the type used for E1601 will be used for future phase III trials comparing investigational therapies to HDI  The definition of noninferiority is critical  Need small cure rate differences

40. CI - 40 Future Trial Designs  Conditional power plays a key role in noninferiority designs  The next ECOG adjuvant phase III trial will be HDI vs –Best vaccine from E1696 –Best vaccine from E2601 –Other regimens  Next trial likely to be designed as noninferiority trial

41. CI - 41 Bayesian Design and Monitoring (1)  Design and monitor trials using a cure rate model within a Bayesian framework  Bayesian approaches offer several advantages in design and monitoring –Incorporate historical data into the sample size calculation –Continuous monitoring –No significance level inflation

42. CI - 42 Bayesian Design and Monitoring (2)  Abundance of historical data on HDI from E1684, E1690, and E1694  Construct appropriate prior distributions for the HDI effect using these data  Prior distributions can be incorporated in the sample size calculations and will often result in a smaller sample size than a traditional design

43. CI - 43 Bayesian Design and Monitoring (3)  Bayesian interim monitoring rules can be easily developed  The Bayesian paradigm allows us to assess the posterior probability that a treatment works, given the current data  Posterior probabilities can be presented at every DMC meeting  No inflation of significance levels