How should efficacy of new adjuvant therapies be evaluated in colorectal cancer?

1. How should efficacy of new adjuvant therapies be evaluated in colorectal cancer? Marc Buyse, ScD IDDI, Brussels, Belgium Based on Daniel Sargent’s talks at ODAC in May 2004and ASCO in June 2004

2. Hypothesis Disease-free survival (DFS), assessed after 3 years, is appropriate to replace overall survival (OS) as an endpoint in adjuvant colon trials (i.e. 3-year DFS is a valid “surrogate endpoint” for 5-year OS)

3. Surrogate Endpoints • Multiple statistical methods proposed • Prentice’s definition and criteria1 • Freedman’s proportion explained2 • Begg and Leung’ concordance 3 • Buyse et al’s correlation 4 • No agreement about best practice 1 Stat Med, 1989. 2 Stat Med, 1992. 3 JRSSA, 2000. 4 Biometrics 1998, Biostatistics 2000, JRSSC, 2001.

4. Prentice criteria • An endpoint can be used as a surrogate if • it predicts the final endpoint • it fully captures the effect of treatment upon the final endpoint • But, how is this verified? Ref: Prentice, Stat Med, 1989.

5. Proportion explained The proportion explained is defined as the proportion of treatment effect that is captured by a surrogate. But, the associated mathematical construct (the change in a model parameter) is flawed. Ref: Freedman et al, Stat Med, 1992.

6. Concordance of results ‘The validity of a surrogate endpoint should be judged by the probability that the trial results based on the surrogate endpoint alone are ‘concordant’ with the trial results that would be obtained if the true endpoint were observed and used for the analysis’ But, concordance of hypothesis tests is driven by their power Ref: Begg and Leung, JRSSA, 2000.

7. Correlation approach An acceptable surrogate must satisfy two conditions: The surrogate must predict the true endpoint The effect of treatment on the surrogate must predict the effect of treatment on the true endpoint Refs: Buyse and Molenberghs, Biometrics 1998; Buyse et al, Biostatistics 2000.

8. Trial characteristics • 33 Arms • 9 no treatment control • 24 ‘Active’ rx • Median follow-up 8 years • 5 year data on 93% of patients • Due to inconsistent long-term follow-up all analyses censored at 8 years

9. Trial First Accrual Treatment Arm(s) N NCCTG 1978 Control vs 5 - FU/lev 247 784852 INT 0035 1985 Control vs 5 - FU/lev 926 NCCTG 1988 Control vs 5 - FU/CF 408 874651 Siena 1985 Control vs 5 - FU/CF 239 NCIC 1987 Control vs. 5 - FU/CF 359 FFCD 1982 Control vs. 5 - FU/CF 259 NSABP C01 1977 Control vs. MOF 773 NSABP C02 1984 Control vs. PVI 5 - FU 718 NSABP C03 1987 MOF vs 5FU/CF 1081 NSABP C04 1989 5FU/Lev/CF 2151 NSABP C05 1991 5FU/CF vs + IFN 2176 GIVIO 1989 Control vs 5 - FU/CF 867 NCCTG 1989 5FU/Lev/ CF 915 894651 NCCTG 1993 5FU/Lev/CF 878 914653 SWOG 1994 5FU/LEV/CF 1078 9415 Total 12915 Total: 33 treatment arms

10. Patient Characteristics • Age • < 50: 2237 (17%) • 50-59: 3487 (27%) • 60-69: 5039 (39%) • > 70: 2071 (16%) • Treatment • Control: 2454 (18%) • Active: 11610 (82%) • Gender • M: 7568 (54%) • F: 6496 (46%) • Stage • I: 210 (2%) • II: 5137 (36%) • III: 8714 (62%)

11. Recurrence rate by 6 mo intervals 8 7.2 6.9 7 6 5.6 5 4 Recurrence Rate (%) 4 3.5 3.2 3 2.2 2 2 1.3 1.2 0.9 0.8 1 0.5 0.5 0.4 0.3 0 0 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8 Years after randomisation

12. 3 year DFS vs 5 year OS 0.8 2 0.75 R = 0.86 r = 0.89 0.7 Overall Survival 0.65 0.6 0.55 0.5 0.5 0.55 0.6 0.65 0.7 0.75 0.8 Disease-Free Survival

13. Parameter Estimate P - value Intercept 0.03 0.048 Slope 0.94 <0.001 3 year DFS vs 5 year OS • Regression equation: • 5 year OS= 0.03+0.94*3 year DFS • Correlation 0.89, R2 = 0.86

14. 3 year DFS vs 5 year OS • On an arm-by-arm basis: • 3 year DFS excellent predictor of 5 year OS • Formal approaches suggest surrogacy • Event rates virtually identical • No impact on sample size • Power for DFS will adequately power for OS

15. 1.3 2 R = 0.87 1.2 r = 0.89 1.1 1 Overall Survival Hazard Ratio 0.9 0.8 0.7 0.6 0.5 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 Hazard ratios: DFS vs OS Disease-Free Survival Hazard Ratio

16. Parameter Estimate P - value Intercept 0.092 0.24 Slope 0. 93 <0.001 Hazard ratios: DFS vs OS • Regression equation: • OS HR = 0.09 + 0.93 * DFS HR • Correlation 0.89, R2 = 0.87

17. Hazard ratios: DFS vs OS OS HR attenuated from DFS HR toward unity in 12 of 18 comparisons

18. Hazard ratios: DFS vs OS • As an endpoint for comparison: • Hazard ratio for DFS an excellent predictor of HR for OS, with slight attenuation • Marginally significant improvements in 3 year DFS may not translate into improvements in 5 year OS

19. Predicted Overall Survival Hazard Ratio Actual Overall Survival Hazard Ratio Predicted and Actual OS Hazard Ratios 1,6 1,4 1,2 1 Hazard Ratio 0,8 0,6 0,4 0,2 C03 C02 C01 C05 NCIC N-78 N-87 N-91 FFCD GIVIO SIENA S9415 C04 c2 C04 c1 N-89 c3 N-89 c2 N-89 c1 INT-0035

20. Discussion • Disease-Free Survival an excellent predictor of Overall Survival • Meets most formal definitions of surrogacy • Modest attenuation of treatment effect between the two endpoints • Model allows prediction of OS effect based on DFS effect

21. Discussion • Is Overall Survival the most desirable endpoint? • It may be the ultimate goal of any therapy for life-threatening disease • But, it is highly insensitive • True treatment benefit may be confounded by successive lines of therapy

22. Collaborators • S Wieand, M O’Connell - NSABP • J Benedetti - SWOG • R Labianca - Ospedali Riuniti (Italy) • D Haller - ECOG • L Shepherd - NCIC • JF Seitz - University of the Mediterranean (France) • G Francini - University of Siena (Italy) • A de Gramont - Hospital Saint Antoine (France) • R Goldberg - NCCTG/UNC • M Buyse - IDDI (Belgium) • Acknowledgement: E Green (Mayo)