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
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
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
CI - 4 Plateau for E1684 Relapse-Free Survival Time interval and no. events/no. at risk Group 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/ Time, yr IFN vs Observation: P 2 =.02, P 1 =.01, HR = 1.38 Proportion alive and relapse free Observation Interferon
CI - 5 Plateau for E1684 Overall Survival Time, yr Proportion alive IFN vs Observation: P 2 =.18, P 1 =.09, HR = 1.22 Time interval, no. events/no. at risk Group 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
CI - 6 Plateau for E1690 Relapse-Free Survival Time, yr Proportion alive and relapse free IFN vs Observation: P 2 =.09, HR = 1.24 Time interval, no. events/no. at risk Group 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
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
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
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
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
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
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 yr for RFS and 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)
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
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
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
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 < Overall survival HDI vs Obs LDI vs Obs InformationNumber of Nominal Information Number of Nominal time recurrences significance < <
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
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
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
CI - 20 E1694 Design Assumptions Median time Cure rate, % to event, yr HDI GMK Relapse-free survival Overall survival
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 < < H 0 = Null hypotheses. H 1 = Alternative hypothesis.
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 < < H 0 = Null hypotheses. H 1 = Alternative hypothesis.
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
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
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
CI - 26 E1697 Statistical Design Assumptions (3) Median time Cure rate, % to event, yr Obs HDI Relapse-free survival Overall survival
CI - 27 E1697 Sequential Monitoring Plan for RFS Upper bound Nominal Real Information Relapses for earlysignificance time, yr time under H 1 stopping level < H 1 = Alternative hypothesis.
CI - 28 E1697 Sequential Monitoring Plan for OS Upper bound Nominal Real InformationDeaths for earlysignificance time, yr time under H 1 stopping level < H 1 = Alternative hypothesis.
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
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
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
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.
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
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
CI - 35 Statistical Design for E1601 (4) Median time Cure rate, % to event, yr 1-yr HDI 1-mo HDI Relapse-free survival
CI - 36 Sequential Monitoring Plan for E1601 (1) Nominal Real Information Relapses Upper significance time, yr time under H 1 bound § level §Upper bound for rejecting noninferiority in favor of I year of HDI.
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
CI - 38 Sample Sizes Under Several Design Scenarios to Achieve 95% Power Cure rate Difference in median Sample difference, % time to event, % size
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
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
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
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
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