ISCB Vaccines Sub

Committee Web Seminar Series
November 7
, 2012
Assessing Immune Correlates of
Protection Via Estimation of the Vaccine
Efficacy
Curve
Peter Gilbert
Fred
Hutchinson Cancer Research Center and
University of Washington,
Department
of Biostatistics
Outline
1.
Introduction:
Concepts and definitions of immune
correlates/surrogate endpoints
2.
Evaluating an immune correlate of protection via the vaccine
efficacy curve
3.
Statistical methods
2
Context: Preventive Vaccine Efficacy Trial
•
Primary Objective
–
Assess
VE:
Vaccine Efficacy to
prevent pathogen

specific
disease
•
Secondary Objective
–
Assess vaccine

induced
immune responses as
correlates of protection
Randomize
Vaccine
Measure
immune
response
Follow for clinical endpoint
(pathogen

specific disease)
Receive
inoculations
Placebo
3
Importance of an Immune Correlate
•
Finding an immune correlate is a central goal of vaccine research
–
One of the 14 ‘Grand Challenges of Global Health’ of the NIH & Gates
Foundation (for HIV, TB, Malaria)
•
Immune correlates useful for:
–
Shortening trials and reducing costs
–
Guiding iterative development of vaccines between basic and clinical
research
–
Guiding regulatory decisions
–
Guiding immunization policy
–
Bridging efficacy of a vaccine observed in a trial to a new setting
•
Pearl (2011,
International Journal of Biostatistics
) suggests that
bridging is
the
reason for a surrogate endpoint
4
Two Major Concepts/Paradigms for Surrogate
Endpoints
Causal agent paradigm
(e.g., Plotkin
, 2008,
Clin
Infect Dis
)
Causal agent of protection
=
marker
that
mechanistically
causes
vaccine efficacy against the clinical endpoint
Prediction
paradigm
(e.g., Qin
et al., 2007,
J Infect Dis
)
Predictor of protection
=
marker
that
reliably predicts
the level
of
vaccine efficacy against the clinical endpoint
Both are extremely useful for
vaccine
development, but are
assessed using different
approaches
For the goal of statistical assessment of surrogate endpoint validity
in an efficacy trial, the prediction paradigm is used
As in the statistical literature, a good surrogate endpoint allows
predicting VE from the vaccine effect on the surrogate
5
Immune Correlates Terminology: Contradictions
Qin et al. (2007)
•
Correlate (of risk)
= measured
immune response that predicts
infection in the vaccine group
•
Surrogate
= measured immune
response that can be used to
reliably predict VE (may or may
not be a mechanism of
protection)
Plotkin (2008)
•
Correlate (of protection)
=
measured immune response that
actually causes protection
(mechanism of protection)
•
Surrogate
= measured immune
response that can be used to
reliably predict VE (is definitely
not a mechanism of protection)
Qin et al. correlate
Plotkin correlate [very different]
Qin et al. surrogate
偬潴歩k畲牯条瑥t
6
Reconciliation of Terminology:
Plotkin and Gilbert (2012,
Clin
Inf
Dis
)
7
Term
Synonyms
Definition
CoP
Correlate of
Protection
Predictor of
Protection;
Good Surrogate
Endpoint
An immune marker statistically correlated
with vaccine efficacy (equivalently
predictive of vaccine efficacy
)*
that may or
may not be a mechanistic causal agent of
protection
mCoP
Mechanistic
Correlate of
Protection
Causal Agent of
Protection; Protective
Immune Function
A
CoP
that is mechanistically causally
responsible for protection
nCoP
Non

Mechanistic
Correlate of
Protection
Correlate of Protection
Not Causal; Predictor
of Protection Not
Causal
A
CoP
that is not a mechanistic causal agent
of protection
*A
CoP
can be used to accurately predict the level of vaccine efficacy conferred to
vaccine recipients (individuals or subgroups defined by the immune marker level
).
Thus a
CoP
is a surrogate endpoint in the statistical literature, and may be assessed
with the Prentice framework or the principal stratification framework.
A Predictive Surrogate/
CoP
May or May Not be a
Mechanism of Protection*
Definition of a
CoP
:
An endpoint that can be used to reliably predict the vaccine
effect on the clinical endpoint
Plotkin
and Gilbert (2012,
Clin
Inf
Dis
) Figure 1. A correlate of protection (
CoP
)
may either be a mechanism of protection, termed
mCoP
, or a non

mechanism of
protection, termed
nCoP
, which predicts vaccine efficacy through its (partial)
correlation with another immune response(s) that mechanistically protects.
8
Many Ways for a
CoR
to Fail to be a
CoP
“A Correlate Does Not a Surrogate Make”
–
Tom Fleming
1.
The
biomarker is not in the pathway of the intervention's
effect, or is insensitive to its
effect
–
E.g., the immunological assay is noisy
2.
The biomarker is not in the causal pathway of the
exposure/infection/disease process
–
E.g., the antibody response neutralizes serotypes of the
pathogen that rarely expose trial participants but fails to
predominantly exposing serotypes
3.
The intervention has mechanisms of action independent
of the disease
process
–
E.g., other immunological functions not measured by the assay
are needed for protection
9
Catastrophic Failure of a
CoR
to be a
CoP
:
the ‘Surrogate Paradox’
•
Surrogate Paradox:
T
he vaccine induces an immune response,
the immune response is inversely correlated with disease risk in
vaccinees
, but VE < 0%
Three Causes of the Surrogate Paradox*
1.
Confounding of the association between the potential
surrogate and the clinical endpoint
2.
The
vaccine positively affects both the surrogate and the
clinical endpoint, but for different sets of subjects
3.
The vaccine may have a negative clinical effect in ways not
involving the potential surrogate
10
*From Tyler
VanderWeele
“There is a plague on Man, the opinion that he
knows something.”
− Michel
de Montaigne (1580,
Essays
)
Outline
1.
Introduction:
Concepts and definitions of immune
correlates/surrogate endpoints
2.
Evaluating an immune correlate of protection via the vaccine
efficacy curve*
3.
Statistical methods
*Gilbert, Hudgens, Wolfson (2011,
J Inter Biostatistics
) discussed the
scientific value of the vaccine efficacy curve for vaccine development
12
Two Frameworks for Assessing a
CoP
from a Single Vaccine
Efficacy Trial: Prentice
& Principal Stratification (PS)
Key Issue: Do trial participants have prior exposure to the pathogen
under study?
If Yes, immune responses vary for both vaccine and placebo
recipients
In this case, the Prentice and PS frameworks both apply
If No, immune responses vary for
vaccinees
only, and the Prentice
framework does not apply (Chan et al., 2002,
Stats Med
)
In this case, only the PS framework applies
In this talk we consider the PS approach in both settings
13
Concept of PS Framework: Assess Association of Individual

Level Vaccine Effects on the Surrogate and Clinical Endpoint
Vaccine Effect on Immune Response Marker for an Individual
Vaccine Effect on Clinical Endpoint
14
Probability an individual is protected
Definition of a Principal Surrogate/Principal
CoP
•
Define the vaccine efficacy surface as
VE(s1, s0) = 1
–
•
Interpretation:
Percent reduction in clinical risk for
a vaccinated
subject with markers (s1, s0) compared to if s/he had not been
vaccinated
•
Definition:
A
p
rincipal
CoP
is a marker with large variability of
VE(s1
, s0) in (s1,
s0)
•
Another useful property is
VE(s1 = s0) = 0
–
This property is Average Causal Necessity: No vaccine effect on
the marker implies no vaccine efficacy
Risk of
clinical endpoint
for
vaccinees
for subgroup with marker effect (s1, s0)
Risk of
clinical endpoint for
placebos
for subgroup with marker effect (s1, s0)
15
Marker Useless as a
CoP
CEP
R
(v
1
, v
0
)
V
E(s1, s0)
16
CEP
R
(v
1
, v
0
)
V
E(s1, s0)
Marker that is an Excellent
CoP
17
Simplest Way to Think About the PS Framework for
Assessing a
CoP
: It’s Simply Subgroup Analysis
•
Conceptually the analysis assesses VE in subgroups defined by the
vaccine effect on the marker
–
Evaluate if and how VE varies with ‘baseline’ subgroups defined
by
(S1, S0)
–
Principal stratification makes
(S1, S0)
equivalent to a baseline
covariate
•
A useful
CoP
will have strong effect modification, i.e., VE(s1, s0)
varies widely in (s1, s0)
•
It would be even more valuable to identify actual baseline covariates
that well

predict
VE,
but it’s much more likely that a response to
vaccination well

predicts VE
18
Simplified Definition of a Principal Surrogate/Principal
CoP
:
Ignore the Immune Response under Placebo, S0
•
Define the vaccine efficacy curve as
VE(s1) = 1
–
•
Interpretation:
Percent reduction in clinical risk for
a vaccinated
subject with markers s1 compared to if s/he had not been
vaccinated
•
Definition:
A
p
rincipal
CoP
is a marker with large variability of
VE(s1)
in
s1
•
The vaccine efficacy curve is useful in both settings that participants have prior
exposure to the pathogen or not
•
If no prior exposure, then VE(s1, s0) = VE(s1), such that the vaccine efficacy
surface simplifies to the vaccine efficacy curve
Risk of
clinical endpoint
for
vaccinees
for subgroup with marker s1
Risk of
clinical endpoint for
placebos
for subgroup with marker s1
19
Vaccine Efficacy Curve: Assess How VE Varies in the Marker
Under Vaccination
Marker level s1
V
E(s1)
Black
marker: worthless
as surrogate
Green
and
blue
markers
satisfy causal necessity
Blue
marker: very good
surrogate
20
Excellent
CoP
:
Sets the Target for Improving the Vaccine
Marker level s1
Black
marker: worthless
as surrogate
Green
and
blue
markers
satisfy causal necessity
Blue
marker: very good
surrogate
V
E(s1)
Target:
Improve the vaccine regimen by increasing the
percentage of
vaccinees
with high immune responses
21
Knowledge of
a
CoP
Guides Future Research to
Develop Improved Vaccines
Identification of a good
CoP
in an efficacy trial is the ideal primary
endpoint in follow

up Phase I/II trials of refined vaccines
It also generates a
bridging hypothesis:
If a future vaccine is
identified that generates higher marker levels in more vaccinated
subjects, then it will have improved overall VE
22
Using the
CoP
for Improving the Vaccine
Regimen
Original Vaccine
New Vaccine 1
New Vaccine 2
Marker levels
23
Using the
CoP
for Improving the
Vaccine
Regimen
Suppose each new vaccine is tested in an efficacy trial
Under the bridging hypothesis we expect the following efficacy
results:
This is the idealized model for using a
CoP
to iteratively improve a
vaccine regimen
Original Vaccine New Vaccine 1 New Vaccine 2
Marker level Marker level Marker level
Estimated VE
Overall TE = 75%
Overall TE = 50%
Overall TE = 31%
24
Outline
1.
Introduction:
Concepts and definitions of immune
correlates/surrogate endpoints
2.
Evaluating an immune correlate of protection via the vaccine
efficacy curve
3.
Statistical methods
25
Challenge to Evaluating a Principal
C
oP
: The Immune Responses to
Vaccine are Missing for Subjects Assigned Placebo
•
Accurately filling in the unknown immune responses is needed
to evaluate a principal
C
oP
•
Two approaches to filling in the missing data (Follmann, 2006,
Biometrics
):
–
BIP (Baseline immunogenicity predictor):
At baseline, measure a
predictor(s) of the immune response in both
vaccinees
and placebos
–
CPV (Close

out placebo vaccination):
At study closeout, vaccinate
disease

free placebo recipients and measure the immune response
26
Example of a Good BIP:
Antibody Responses to Hepatitis A and B Vaccines*
*
Czeschinski
et al.
(
2000,
Vaccine
) 18:1074

1080
Spearman rank
r
= .85
No cross

Reactivity
N=75 subjects
27
Baseline Immunogenicity Predictor
28
Schematic of Baseline Immunogenicity Predictor (BIP) &
Closeout Placebo Vaccination (CPV) Trial Designs*
W
W


S=S(1)
S
c
*Proposed by
Follmann
(2006,
Biometrics
)
1
1
+
1
1
+
S(1)
CPV Approach
BIP Approach
BIP Approach
Vx
Vx
29
Literature on Statistical Methods for Estimating the
Vaccine Efficacy Curve via BIP and/or CPV
Article
Comment
1. Follmann (2006,
Biometrics)
Binary outcome; BIP&CPV; Estimated likelihood
2. Gilbert and Hudgens
(2008,
Biometrics)
Binary outcome; BIP; Estimated
likelihood; 2

phase sampling
3. Qin,
Gilbert, Follmann,
Li
(2008, Ann
Appl
Stats)
Time

to

event outcome (Cox model); BIP&CPV; Estimated likelihood; 2

phase sampling
4. Wolfson and Gilbert
(2010, Biometrics)
Binary outcome; BIP&CPV; Estimated likelihood; 2

phase sampling; relaxed
assumptions
5. Huang and Gilbert
(2011, Biometrics)
Binary outcome; BIP&CPV; Estimated likelihood; 2

phase sampling; relaxed
assumptions; compare markers
6. Huang,
Gilbert, Wolfson
(2012, under revision)
Binary outcome; BIP&CPV;
Pseudolikelihood
; 2

phase sampling; relaxed
assumptions; marker sampling design
7. Miao, Li, Gilbert,
Chan
(2012, under revision)
Time

to

event outcome (Cox model); BIP; Estimated likelihood with
multiple imputation; 2

phase sampling
8. Gabriel and Gilbert
(2012, submitted)
Time

to

event outcome (
Weibull
model)
; BIP+CPV; Estimated likelihood
and
pseudolikelihood
; 2

phase
sampling; threshold models
30
Summary of One of the Principal Stratification
Methods:
Gilbert and Hudgens (2008,
Biometrics
) [GH]
31
Notation (Observed and Potential Outcomes)
Z
= vaccination assignment (0 or 1; placebo or vaccine)
W
= baseline immunogenicity predictor of
S
S
= candidate surrogate endpoint/immune
CoP
measured at
time
after randomization
Y
= clinical endpoint (0 or 1; 1 = experience event during follow

up)
S(Z
)
= potential
surrogate endpoint under
assignment
Z
, for
Z
=0,1
Y(Z
)
= potential clinical endpoint
under assignment
Z
, for
Z
=0,1
32
Assumptions
A1
Stable Unit Treatment Value Assumption (SUTVA):
(S
i
(1), S
i
(0), Y
i
(1), Y
i
(0)) is independent of the treatment assignments
Z
j
of
other subjects
−
A1
implies “consistency”: (S
i
(
Z
i
), Y
i
(
Z
i
)) = (S
i
, Y
i
)
A2
Ignorable Treatment Assignment:
Z
i
is independent of (S
i
(1), S
i
(0), Y
i
(1), Y
i
(0))
−
A2
holds for randomized blinded trials
A3
Equal individual clinical risk up to time
that S is measured
(zero vaccine efficacy for any individual up to time
)
33
Definition of a Principal Surrogate/Principal
CoP
(
Frangakis
and Rubin, 2002; Gilbert and Hudgens, 2008)
•
Define
risk
(1)
(s
1
, s
0
) = Pr(Y(1) = 1S(1) = s
1
, S(0) = s
0
)
risk
(0)
(s
1
, s
0
) = Pr(Y(0) = 1S(1) = s
1
, S(0) = s
0
)
•
A contrast in risk
(1)
(s
1
, s
0
) and risk
(0)
(s
1
, s
0
) is a causal effect on Y for the
population {S(1) = s
1
, S(0) = s
0
}
•
VE(s
1
, s
0
) = 1

risk
(1)
(s
1
, s
0
) / risk
(0)
(s
1
, s
0
)
•
A good
CoP
has
VE(s
1
, s
0
)
varying widely in (s
1
, s
0
)
[i.e., a large amount of
effect modification]
•
Also,
with
VE(s
1
)
= 1

risk
(1
)
(
s
1
)
/ risk
(0
)
(
s
1
), a good
CoP
has
VE(s
1
)
varying
widely in s
1
•
These definitions allow
for a spectrum of principal
CoPs
,
some more
useful than others, depending
on the degree of effect modification
34
Statistical Methods:
Build on Two

Phase Sampling Methods
•
Case

cohort or case

control sampling (Ignore S0)
−
(W, S(1))
measured in
•
All infected vaccines
•
Sample of uninfected vaccines
−
W
measured in
o
All infected placebos
o
Sample of uninfected placebos
•
2

Phase designs
(E.g., Prentice, 1986,
Biometrika
; Kulich and
and
Lin,
2004,
JASA;
Breslow
et al., 2009,
AJE, Stat Biosciences
)
−
Phase 1: Measure inexpensive covariates in all subjects
−
Phase 2: Measure expensive covariates
X
in a sample of subjects
•
Our application
−
Vaccine Group: Exactly like 2

phase design with
X = (W, S(1))
−
Placebo Group: Like 2

phase design with
X = (W, S(1))
and
S(1)
missing
35
IPW Case

Cohort Methods Do Not Apply:
Hence we use
a Full Likelihood

Based Method
•
Most of the published 2

phase sampling/case

cohort failure time
methods cannot be extended to estimate the VE curve
–
This is because they are inverse probability weighted (IPW) methods, using
partial likelihood score equations that sum over subjects with phase

2 data
only, which assume that every subject has a positive probability that S(1) is
observed
–
However, all placebo subjects have zero

probability that S(1) is observed
•
To deal with this problem, the published methods all use full
likelihood, using score equations that sum over all subjects
36
Maximum Estimated Likelihood* with
BIP
•
Posit models for
risk
(1)
(s
1
,0;
)
and
risk
(0)
(s
1
,0;
)
•
Vaccine arm:
−
(
W
i
, S
i
(1))
measured:
Likld
contribn
risk
(1)
(S
i
(1), 0;
)
−
(
W
i
, S
i
(1))
not measured:
risk
(1)
(s
1
, 0;
)
dF
(s
1
)
•
Placebo arm:
−
W
i
measured:
Likld
contribn
risk
(0)
(s
1
, 0;
)
dF
SW
(s
1

W
i
)
−
W
i
not measured:
r楳i
(0)
(s
1
, 0;
)
dF
(s
1
)
•
L(
, F
SW
, F
) =
i
risk
(1)
(S
i
(1),0;
)
Yi
(1

risk
(1)
(S
i
(1),0;
))
1

Yi
]
Zi
}
i
[
Vx
subcohort
]
risk
(0)
(s
i
,0;
)
dF
SW
(s
1
W
i
)
Yi
(1

risk
(0)
(s
1
,0;
))
dF
SW
(s
1
W
i
)
1

Yi
]
1

Zi
}
i
[Plc
subcohort
]
risk
(1)
(s
i
,0;
)
dF
(s
1
)
Yi
(1

risk
(1)
(s
1
,0;
))
dF
(s
1
)
1

Yi
]
Zi
}
1

i
[
Vx
not
subcohort
]
risk
(0)
(s
i
,0;
)
dF
(s
1
)
Yi
(1

risk
(0)
(s
1
,0;
))
dF
(s
1
)
1

Yi
]
1

Zi
}
1

i
[
Plc not
subcohort
]
*
Pepe
and Fleming (1991) an early article on estimated likelihood
37
Maximum Estimated Likelihood Estimation
(MELE)
•
Likelihood L(
,
F
SW
, F
)
−
is parameter of interest [VE curve depends only on
]
−
F
SW
and
F
are nuisance parameters
Step 1:
Choose models for
F
SW
and
F
and estimate
them based on
vaccine arm data
Step 2:
Plug the consistent estimates of
F
SW
and F into the likelihood,
and maximize it in
−
e.g., EM algorithm
Step 3:
Estimate the variance of the MELE of
, accounting for the
uncertainty in the estimates of
F
SW
and
F
−
Bootstrap
38
Example: Nonparametric Categorical Models
•
Assume:
−
S and W categorical with J and K levels; S
i
(0)=1 for all
i
[No prior exposure scenario: category 1 = negative
response]
−
Nonparametric models for P(S(1)=j, W=k)
−
A4

NP:
Structural models for risk
(z)
(for z=0, 1)
risk
(z)
(j, 1, k;
) =
zj
+
’
k
for j=1, …, J; k=1, …, K
Constraint: 0 ≤
zj
+
’
k
≤ 1 and
k
’
k
= 0 for
identifiability
A4

NP
asserts
n
o interaction: W has the same effect on
risk for the 2 study groups (untestable)
39
Vaccine Efficacy Curve for Categorical Marker
CEP
risk
(j, 1) = log (
avg

risk
(1)
(j, 1
) /
avg

risk
(0)
(j, 1
))
where
avg

risk
(z)
(j, 1
) = (1/K)
k
risk
(z)
(j, 1, k;
)
VE(j, 1) = 1
–
exp{
CEP
risk
(j, 1)}
The vaccine efficacy curve is VE(j, 1) at each level j
of S(1)
40
Tests for the Vaccine Efficacy Curve
VE(j, 1) Varying in j
•
Wald tests for whether a biomarker has any surrogate value
−
Under the null,
PAE(w) = 0.5
and
AS = 0
−
Z = (Est. PAE(w)
–
0.5)/
s.e
.(Est. PAE(w))
−
Z = Est. AS/
s.e
.(Est. AS)
o
Estimates obtained by MELE; bootstrap standard errors
•
For nonparametric case
A4

NP, test H0:
CEP
risk
(j, 1) = 0
vs
H1:
CEP
risk
(j, 1)
increases in j
(like
Breslow

Day trend test)
T =
j>1
(j

1) {Est.
0j
–
(Est.
0j
+ Est.
1j
)(Est.
z0
/(Est.
z0
+ Est.
z1
))}
divided by bootstrap
s.e
.
Est.
z
= (1/J)
j
zj
41
Simulation Study:
Vax004 HIV Vaccine Efficacy Trial*
•
Step 1:
For all N=5403 subjects, generate (
W
i
, S
i
(1)) from a
bivariate
normal with means (0.41, 0.41),
sds
(0.55, 0.55), correlation
= 0.5, 0.7, or 0.9
sd
of 0.55 chosen to achieve the observed 23% rate of left

censoring
Values of
W
i
, S
i
(1) < 0 set to 0; values > 1 set to 1
•
Step 2:
Bin
W
i
and S
i
(1) into quartiles
Under model A4

NP generate
Y
i
(Z) from a Bernoulli(
zj
+
’
k
) with the
parameters set to achieve:
o
P(Y(1) = 1) = 0.067
and P(Y(0) = 1) = 0.134 (overall VE = 50%)
o
The biomarker has either (
i
) no or (ii) high surrogate value
*Flynn et al. (2005, JID), Gilbert et al. (2005, JID)
42
Simulation Plan
•
Scenario (i) (no surrogate value)
−
CEP
risk
(j, 1) =

0.69 for j = 1, 2, 3, 4
−
i.e., VE(j, 1) = 0.50 for j = 1, 2, 3, 4
•
Scenario (ii) (high surrogate value)
−
CEP
risk
(j, 1) =

0.22,

0.51,

0.92,

1.61 for j = 1, 2, 3, 4
−
i.e., VE(j, 1) = 0.2, 0.4, 0.6, 0.8 for j = 1, 2, 3, 4
43
Simulation Plan
•
Step 3: Create case

cohort sampling (3:1 control: case)
−
Vaccine group:
(W, S(1)) measured in all infected
(n=241) and a random sample of 3 x 241 uninfected
−
Placebo group:
W measured in all infected (n=127) and
a random sample of 3 x 127 uninfected
•
The data were simulated to match the real
VaxGen
trial as
closely as possible
44
Model A4

NP Simulation Results: Bias and
Coverage Probabilities [Table 1 of GH]
45
Model A4

NP Simulation Results: Power to Detect the
VE(j,1) curve varying in j [Table 2 of GH]
Trend
tests for VE(j, 1) increasing in j:
Power 0.83, 0.99, > 0.99 for
= 0.5, 0.7,
0.9
46
Conclusions of Simulation Study
•
The MELE method of Gilbert and Hudgens performs well for
realistically

sized Phase 3 vaccine efficacy trials, with accuracy,
precision, and power improving sharply with the strength of
the BIP (desire high
)
•
This shows the importance of developing good BIPs
•
R code for the nonparametric method available at the
Biometrics website and at
http://faculty.washington.edu/peterg/SISMID2011.html
47
•
Crossing over more placebo subjects improves power of CPV and BIP + CPV
designs
•
There is no point of diminishing returns− steady improvement with more
crossed over, out to complete cross

over
•
If the BIP is high quality (e.g.,
> 0.50), then the BIP design is quite
powerful with only modest incremental gain by adding CPV
•
However, CPV has additional value beyond efficiency improvement:
–
Helps in diagnostic tests of structural modeling assumptions (A4)
–
May help accrual and enhance ethics
–
May adaptively initiate crossover, e.g., as soon as the lower 95%
confidence limit for VE exceeds 30%
•
Pseudoscore
method superior to estimated likelihood method (Huang,
Gilbert, Wolfson, 2012, under revision); recommend this method in practice
–
Happy to provide the code for this method (for BIP, CPV, BIP+CPV
)
Remarks on Power for Evaluating a Principal
Surrogate Endpoint (For All Methods

Beyond GH)
48
Concluding Remarks
•
Opportunity to improve assessment of immune
CoPs
by increasing
research into developing BIPs
–
The better the BIP, the greater the accuracy and precision for estimating
the vaccine efficacy curve
49
Some Avenues for Identifying Good BIPs
•
Demographic factors
–
E.g., age, gender, BMI, immune status
•
Host immune genetics
–
E.g., HLA type and MHC binding prediction machine learning methods
for predicting T cell responses
•
Add beneficial licensed vaccines to efficacy trials and use known correlates
of protection as BIPs (
Follmann’s
[2006] original proposal)
–
The HVTN is exploring this strategy in a Phase 1 trial in preparation for
efficacy trials
•
Develop ‘pathogen exposure history’ chip
•
In efficacy trials where participants have prior exposure to the pathogen,
measure the potential
CoP
at baseline and use it as the baseline predictor
–
E.g., Varicella Zoster vaccine trials: baseline
gpELISA
titers strongly
predict post

immunization titers
–
Miao, Li, Gilbert, Chan (2012, under review) and Gabriel and Gilbert
(2012, in preparation) estimate the Zoster vaccine efficacy curve using
this excellent BIP (will constitute an excellent example when
published)
50
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