Assessing Surrogacy
in CKD*
Tom Greene, Ph.D.
University of Utah
*Joint work with Marshal Joffe, Liang Li,
Andrew Levey, and Lesley Stevens
Concept of Surrogate Endpoint
• The most relevant clinical outcome may
be difficult to use in an RCT because:
– measurement is costly or invasive
– long follow-up required
– large N required
• A surrogate endpoint is an alternative
outcome that is substituted for the true
clinical outcome in an attempt to reduce
these difficulties
GFR (ml/min/1.73m2)
Difficult to Evaluate ESRD in
Trials of Early CKD
Window
for feasible
RCTs with
renal
endpoints
100
50
ESRD
0
14
12
10
8
6
Years from ESRD
4
2
0
Past Problems with Surrogate Endpoints
Disease
Intervention
Surrogate
Clinical
outcome
Arrythmia
Encainide,
Flecainide*
Suppressed
arrhythmias
Increased
mortality
CHF
Milrinone
Improved
cardiac output
Increased
mortality
Osteoperosis
Sodium
Fluoride
Increased bone
More Fractures
mineral density
Failures of Mean Change in GFR as
Surrogate for Renal Endpoints
Incidence of ESRD or Death
Change in GFR from Baseline
Mean (SE) Δ GFR (ml/min/1.73m2)
8
Amlodipine
Ramipril
Amlodipine
Ramipril
40
4
HR = 0.51,
p < 0.001
30
0
20
-4
10
-8
-12
0
0
6
12
24
36
Follow-up Month
48
0
12
24
36
48
Follow-up Month
60
Endpoints for Progression of CKD
• Target Clinical Endpoint:
– Time-to-ESRD (± Death)
• Time-to-Event Surrogate Endpoints:
– 50% reduction in GFR or ESRD (± Death) ?
– Doubling of serum creatinine (SCR) or ESRD
(± Death) ?
• Slope-Based Surrogate Endpoints
– Slope of GFR vs. time ??
– Slope of estimated GFR (from SCR) ??
• Proteinuria Based Surrogate Endpoints ???
Statistical Approaches to
Validation of Surrogate Endpoints
• Individual level association
• Prentice criterion & variations
• Trial-level association
– Uses randomized comparisons
to determine if treatment effect
on UP predicts the treatment
effect on the clinical endpoint
Can be applied in
singe study but subject
to confounding
Truly based on
randomized inference
but usually requires
analyses of multiple
RCTs & effect
modification
Statistical Approaches to Validating
Proteinuria (UP) as Surrogate Endpoint
1) Evaluate individual level association
of UP with the clinical endpoint
 Relationships of initial UP and ΔUP with
progression endpoints for individual
patients
 Evaluated within treatment groups in RCTs
Association of Baseline Albuminuria
with progression in RENAAL*
* Renal endpoint is composite of doubling of SCR, ESRD, or Death
Early Decline in UP Predicts
Better Renal Outcome
RCTs showing Individual-level association of baseline
UP & Δ UP with clinical endpoints
•
•
•
•
•
•
•
CSG Trial – treatment with captopril
MDRD Trial – blood pressure lowering
REIN Trial – treatment with ramipril
AIPRD - 11 trials, treatment with ACEi
IDNT Trial – treatment with irbesartan
RENAAL Trial – treatment with losartan
AASK Trial – treatment with blood pressure
lowering, ramipril, metoprolol, or amlodipine
Limits of Individual Level Association
(Fundamental Limitation of Causal Inference)
T(1)
T(0)
= True endpoint if assigned to treatment
= True endpoint if assigned to control
T(1) – T(0) = Causal effect of treatment on true endpoint
UP(1) = UP given treatment
UP(0) = UP given control
UP(1) – UP(0) = Causal effect of treatment on UP
Treatment effect on UP predicts treatment effect on T
if UP(1) – UP(0) accurately predicts T(1) – T(0)
UP(1) – UP(0)
But cannot observe either
or
T(1) – T(0)
Implicit Causal Model of Individual
Association Approach
Treatment
UP
T
Implicit Causal Model of Individual
Association Approach
Confounders
x
x
Treatment
UP
x
T
Key implicit assumptions of individual association approach:
 UP on causal pathway between treatment and T
 No common causes of UP and T other than the treatment
 No direct effect of treatment on T
Prentice Criterion (1989)
Under regularity assumptions,
H0: No effect of the Treatment on the Surrogate
is equivalent to
H0: No effect of the Treatment on T
if and only if
P: The true clinical endpoint is unrelated to the
treatment after controlling for surrogate
Treatment
Surrogate
(UP)
True
Outcome
T
Variations on Prentice Criterion
Related index (Freedman):
• Proportion of the treatment effect on
the true clinical endpoint explained by
surrogate (PTE)
PTE = 1 -
Treatment effect after controlling for surrogate
Treatment effect not controlling for surrogate
PTE for ACE/ARB Interventions
Study
Intervention
Index
AIRPD
(11 non-diabetic kidney
disease studies)
AASK
(hypertensive nephro.)
RENAAL
(Type II diabetics)
IDNT
(Type II diabetics)
ACE
Ramirpril vs.
Amlodipine
Δ UP
Δ UP
Outcome
PTE
ESRD alone
18%
Doubling SCR
26%
ESRD or Death
41%
Losartan vs.
ESRD, SCR, Death
Δ ALB
Placebo
ESRD alone
89%
51%
Ibesartan vs.
Δ ALB ESRD alone
Placebo
36%
Implied Causal Model of Freedman/Prentice
Approach
Confounders
x
x
Treatment
UP
T
Key Assumption of Implicit Freedman/Prentice Model:
 UP on the causal pathway between A and T
 No common causes of UP and T other than the treatment
Limited Extension of Prentice/Freedman
Approach: Attempt to control confounding
Measured Covariates
Treatment
UP
T
Key Assumptions of Expanded Framework:
 UP on the causal pathway between A and T
 No unmeasured confounders of UP and T
Can then estimate the causal direct effect and the indirect
effect which is mediated by UP, and a properly defined PTE
Fundamental Limitation of
Single-Trial Validation Study
• No true assessment of
generalizability
– Need to look at variations in treatment
and in study population
Statistical Approaches to
Validating a Surrogate Endpoint
Trial-Level Approach*
 Relate treatment effects on true endpoint
to treatment effects on surrogate
 Treatment effects on both UP and T
estimated from randomized comparisons
 Usually requires joint analysis of multiple
studies
* Daniels and Hughes, Stat Med 1997;
Molenberghs et al, 2000 – 2005
Trial-Level Approach
Treatment effect on ESRD
(Log RR)
Ideal Hypothetical Example

 
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   
 



Treatment effect on % Δ UP
Points represent estimated treatment effects in different RCTs
Trial-Level Approach
Treatment effect on ESRD
(Log RR)
Ideal Hypothetical Example

 

   
 



Δ UP for new trial
Treatment effect on % Δ UP
Points represent estimated treatment effects in different RCTs
Trial-Level Approach
Real Example*
* Burzykowski, JRSS A, 2004
Incorporation of Trial Level Can Obtain Direct &
Indirect Effect In Broader Framework
• Trial level approach formulated under “causal
association framework” – just relates treatment
effects on T to treatment effects on UP.
– No need to assume UP is directly on causal pathway.
• Under intermediate variable model, trial level
approach estimates direct and indirect effects
associated with UP even in presence of
uncontrolled confounders
Limits of Trial Level Approach
Treatment effect on ESRD
(Log RR)
• Logic for extrapolation to new studies
works best if new study is similar to prior
studies (e.g., best for “me-too” studies)

  
   
 



Δ UP for new trial
Treatment effect on % Δ UP
Limits of Trial Level Approach
• Requires significant heterogeneity between
studies in “true” treatment effects on UP
Treatment effect on ESRD
(Log RR)
– Requires “effect modification”
– Opposite of typical situation in meta-analysis

  
   
 



Δ UP for new trial
Treatment effect on % Δ UP
Treatment Effect on T (HR)
Consistent Effects on both UP and Clinical
Endpoint Encouraging but Not Convincing
from Perspective of Trial-Based Approach
1
0
Treatment Effect on Δ UP
Looking for Heterogeneity
• Can look for effect modification between and within
studies
• Between interventions:
–
–
–
–
–
ACE vs. Control
CCB vs. Control
Low vs. Usual BP
Low vs. Usual Protein
Immunosuppressive therapies
• Between patient subgroups
– Diabetics, Non-diabetics, Transplant recipients
– Low UP, High UP
– PKD, Glomerular disease, Hypertensive kidney disease,
Interstitial kidney disease
– Younger, Older
– Black, white
Technical Difficulties for
Trial-Level Approach in CKD
• Huge variation in sample sizes between
studies
• Observed treatment effects on clinical
endpoints depend in part of arbitrary study
characteristics – e.g., distribution of GFR at
entry, length of follow-up
• Variation in measurement of UP
• Unclear what is best index of ΔUP (e.g.,
absolute change or % change)
CKD-EPI Project
•
•
•
Collaboration to analyze large
databases of pooled individual-patient
data, including most major RCTs of
CKD-patients
Assess validity of change in
proteinuria as a surrogate marker using
all three statistical approaches
Lesley Stevens to later present
very preliminary results for ACE/ARB
studies
Less
Risky
Potential Uses of “Validated”
Surrogate Endpoints
• Early phase of development of new
interventions
• Exploratory subgroup analyses
More
Risky
• Extension of established findings to related
patient populations with less severe disease
• Extension of established findings to related
interventions
• Establish benefit of new interventions
Conclusions
• Use of surrogate endpoints is necessary
component of clinical research
• Statistical formalisms for addressing validity
of surrogate endpoints are still developing
• Two general statistical approaches:
– Estimate direct & indirect effects under models
that attempt to control for all confounding between
UP & T
– Try to take advantage of effect modification to
determine if treatment effects on UP predict
treatment effects on T
Conclusions
• Biological evidence also very important
• Use of surrogates can be context
specific
– Don’t use BP for testing a lipid lowering
drug
• All uses of surrogate endpoints entail
“extrapolation beyond the data”