Transcript Type the Title of Your Talk Here

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 2004
and ASCO in June 2004
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)
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.
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.
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.
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.
Correlation approach
An acceptable surrogate must satisfy two
conditions:
1. The surrogate must predict the true
endpoint
2. 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.
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
Trial
First Accrual Treatment Arm(s)
N
NCCTG 784852
INT 0035
NCCTG 874651
Siena
1978
1985
1988
1985
Control vs 5-FU/lev
Control vs 5-FU/lev
Control vs 5-FU/CF
Control vs 5-FU/CF
247
926
408
239
NCIC
FFCD
NSABP C01
1987
1982
1977
Control vs. 5-FU/CF
Control vs. 5-FU/CF
Control vs. MOF
359
259
773
NSABP C02
1984
Control vs. PVI 5-FU
718
NSABP C03
NSABP C04
NSABP C05
GIVIO
NCCTG 894651
1987
1989
1991
1989
1989
MOF vs 5FU/CF
5FU/Lev/CF
5FU/CF vs + IFN
Control vs 5-FU/CF
5FU/Lev/CF
1081
2151
2176
867
915
NCCTG 914653 1993
5FU/Lev/CF
878
SWOG 9415
5FU/LEV/CF
1078
1994
Total
12915
Total: 33 treatment arms
Patient Characteristics
• Age
• < 50: 2237 (17%)
• 50-59: 3487 (27%)
• 60-69: 5039 (39%)
• > 70: 2071 (16%)
• Gender
• M: 7568 (54%)
• F: 6496 (46%)
• Treatment
• Stage
• Control: 2454 (18%)
• I: 210 (2%)
• Active: 11610 (82%)
• II: 5137 (36%)
• III: 8714 (62%)
Recurrence rate by 6 mo
intervals
8
7.2
Recurrence Rate (%)
7
6
6.9
5.6
5
4
3.5
4
3.2
3
2.2 2
2
1.3 1.2
1
0.9 0.8
0
0.5 0.5 0.4 0.3
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
3 year DFS vs 5 year OS
0.8
Overall Survival
0.75
0.7
R2= 0.86
r = 0.89
0.65
0.6
0.55
0.5
0.5
0.55
0.6
0.65
0.7
Disease-Free Survival
0.75
0.8
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
Parameter
Estimate
P-value
Intercept
0.03
0.048
Slope
0.94
<0.001
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
Overall Survival Hazard Ratio
Hazard ratios: DFS vs OS
1.3
2
1.2
R = 0.87
1.1
r = 0.89
1
0.9
0.8
0.7
0.6
0.5
0.5
0.6
0.7
0.8
0.9
1
1.1
Disease-Free Survival Hazard Ratio
1.2
1.3
Hazard ratios: DFS vs OS
• Regression equation:
• OS HR = 0.09 + 0.93 * DFS HR
• Correlation 0.89, R2 = 0.87
Parameter
Estimate
P-value
Intercept
0.092
0.24
Slope
0.93
<0.001
Hazard ratios: DFS vs OS
OS HR attenuated from
DFS HR toward unity in
12 of 18 comparisons
C01
C02
C03
C04 c1
C04 c2
C05
FFCD
Disease-Free
Survival
Overall Survival
GIVIO
INT-0035
N-78
N-87
N-89 c1
N-89 c2
N-89 c3
N-91
NCIC
S9415
SIENA
0,2
0,4
0,6
0,8
1
Hazard Ratio
1,2
1,4
1,6
1,8
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
Predicted and Actual OS Hazard
Ratios
1,6
1,4
Hazard Ratio
1,2
1
0,8
0,6
0,4
0,2
Predicted Overall Survival Hazard Ratio
Actual Overall Survival Hazard Ratio
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
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
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)