Assessing Immune Correlates of

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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) = 1|S(1) = s
1
, S(0) = s
0
)



risk
(0)
(s
1
, s
0
) = Pr(Y(0) = 1|S(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
S|W
(s
1
|
W
i
)



W
i

not measured:



r楳i
(0)
(s
1
, 0;

)
dF
(s
1
)


L(

, F
S|W
, 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
S|W
(s
1
|W
i
)
Yi
(1
-


risk
(0)
(s
1
,0;

))
dF
S|W
(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
S|W
, F
)




is parameter of interest [VE curve depends only on

]


F
S|W

and

F

are nuisance parameters

Step 1:


Choose models for
F
S|W

and

F

and estimate

them based on


vaccine arm data

Step 2:

Plug the consistent estimates of
F
S|W

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
S|W

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