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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

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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

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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

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Plateau for E1684Relapse-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 0/3Interferon 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

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Time, yr

IFN vs Observation:P2 = .02, P1 = .01, HR = 1.38

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ObservationObservation

InterferonInterferon

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Plateau for E1684Overall Survival

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Time, yr

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IFN vs Observation:P2 = .18, P1 = .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 0/6Interferon 54/146 19/90 10/70 3/60 2/56 5/52 0/35 0/10

ObservationObservation

InterferonInterferon

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Plateau for E1690Relapse-Free Survival

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IFN vs Observation:P2 = .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

ObservationObservation

InterferonInterferon

8 9

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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

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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

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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

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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

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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

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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)

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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

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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

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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

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Sequential Monitoring Plan for E1690 Relapse-free survival

HDI vs Obs LDI vs Obs

Information Number of Nominal Information Number of Nominaltime recurrences significance time recurrences significance

0.258 65 < 10-5 .310 78 .00001450.517 130 .00101 .517 130 .001010.756 190 .00772 .756 190 .007721.000 252 .0223 1.000 252 .0223

Overall survival

HDI vs Obs LDI vs Obs

Information Number of Nominal Information Number of Nominaltime recurrences significance time recurrences significance

.141 28 < 10-6 .151 30 < 10-6

.398 79 .000146 .398 79 .000146

.623 124 .00308 .623 124 .003081.000 199 .0239 1.000 199 .0239

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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

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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

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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

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E1694 Design Assumptions

Median time Cure rate, % to event, yr

HDI GMK HDI GMK

Relapse-free survival 35.9 45.9 .747 .859

Overall survival 38.4 48.4 1.658 1.907

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E1694 Sequential Monitoring Plan for RFS

Rejection NominalReal Information Relapses Upper probability significancetime, yr time under H1 bound under H0 level

2.01 0.35 149 3.6128 <0.0001 <.0001

2.82 0.61 260 2.6506 0.004 .004

3.87 0.86 366 2.1894 0.012 .014

5.30 1.00 426 2.0536 0.009 .020

0.025

H0 = Null hypotheses.H1 = Alternative hypothesis.

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E1694 Sequential Monitoring Plan for OS

Rejection NominalReal Information Deaths Upper probability significancetime, yr time under H1 bound under H0 level

2.01 0.25 81 4.3326 < 0.0001 < .0001

2.82 0.47 151 3.0738 0.001 .001

3.87 0.74 238 2.3757 0.008 .0109

5.30 1.00 322 2.0132 0.016 .022

0.025

H0 = Null hypotheses.H1 = Alternative hypothesis.

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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

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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

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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

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E1697 Statistical Design Assumptions (3)

Median time Cure rate, % to event, yr

Obs HDI Obs HDI

Relapse-free survival 65 72.5 1.5 1.725

Overall survival 75 82.5 2.5 2.875

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E1697 Sequential Monitoring Plan for RFS

Upper bound Nominal

Real Information Relapses for early significancetime, yr time under H1 stopping level

1.95 0.25 85 4.3326 <.0001

2.92 0.50 169 2.9680 .0030

3.99 0.75 254 2.3617 .0182

5.00 0.90 305 2.1681 .0301

6.00 1.00 339 2.0774 .0377

H1 = Alternative hypothesis.

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E1697 Sequential Monitoring Plan for OS

Upper bound NominalReal Information Deaths for early significancetime, yr time under H1 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

H1 = Alternative hypothesis.

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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

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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

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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

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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.

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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

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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

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Statistical Design for E1601 (4)

Median time Cure rate, % to event, yr

1-yr HDI 1-mo HDI 1-yr HDI 1-mo HDI

Relapse-free survival 63 60 0.90 0.65

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Sequential Monitoring Plan for E1601 (1)

Nominal

Real Information Relapses Upper significancetime, yr time under H1 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.

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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

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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 3 15 4400 5 25 1520 5

20 1820 5 15 2260 7 25 1040 7 20 1220 7 15 140010 25 600 10 20 68010 15 760

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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

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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

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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

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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

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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