Download Statistical Science Issues in HIV Vaccine Trials: Part II

Statistical Science Issues in Preventive HIV
Vaccine Efficacy Trials: Part II
Outline
1.
2.
3.
4.
Efficacy Trial Objectives
Phase IIb vs Phase III
Scientific questions
Approaches to the questions
–
–
–
–
HIV infection endpoint
Post-infection endpoints
Correlates of protective immunity
Strain-specific vaccine efficacy
Note: This talk is restricted to individual-randomized
designs
Introduction
• Study population
– HIV negative, high-risk volunteers
 Homosexual men [e.g., Vax004]
 Intravenous drug users [e.g., Vax003]
 Men, women, adolescents at heterosexual risk
• Study arms
– HIV vaccine versus blinded control
 placebo or a non-HIV vaccine
– Multiple active HIV vaccines
• This talk focuses on a simple 2 arm study of vaccine vs
placebo
Trial Objectives: Efficacy Parameters
• Goal: Collect information on Vaccine Efficacy:
–
–
–
–
VES: Susceptibility
VEP: Progression
VEI:
Infectiousness
VER0: Basic reproduction number
• VES/VEP assessable in standard trial design
• VEI only assessable (directly) by enrolling sexual
partners
• VER0 only assessable under models and assumptions
– E.g., VER0 = 1 – (1-VES)(1-VEI)
= 1 – (1-VES)2.45VL
Trial Objectives: Correlates of
Protective Immunity
• If protection observed, evaluate correlation of vaccineinduced immune responses with protection
– Protection against HIV infection
– Protection against post-infection endpoints (e.g., durable
control of viremia)
• Value of an immunological correlate of protection
– Guide vaccine development
– Improve immunogens iteratively between basic and clinical
research
– Guide regulatory decisions
– Guide immunization policy
– Bridge efficacy of a vaccine observed in a trial to a new setting
 E.g., bridge across vaccine lots, human populations, or viral
populations
Trial Objectives: Strain-Specific
Efficacy
• Evaluate dependency of vaccine protection on
genotypic/phenotypic characteristics of HIV
– Strain-specific protection against infection
– Strain-specific protection against post-infection endpoints
• Value of assessing strain-specific efficacy
–
–
–
–
Guide vaccine development
Guide regulatory decisions
Guide immunization policy
Improve immunogens iteratively between basic and clinical
research
Phase IIb vs Phase III Efficacy Trials
• Phase IIb
~30-60 infections in placebo arm
• Phase III
~125-200 infections in placebo arm
– Goal: Advance promising vaccine to Phase III, or weed it out
– Powered for viral load set-point, but can only detect large VES
(> 60%)
– Not powered to study durability of efficacy to suppress viremia
– Not powered to study correlates of protection or strainspecific efficacy
– Goal: Definitive evaluation to support vaccine licensure decision
– Powered for both infection endpoint (VES > 30%) and viral load
set-point
– Powered for secondary analyses (durability of viremia
suppression, correlates of protection, strain-specific efficacy)
Merck’s HIV Vaccine Project
• Lead vaccine is an Adenovirus type 5 (Ad5) vector
encoding HIV-1 gag, pol and nef genes
• Goal: To induce broad cell mediated immune (CMI)
responses against HIV that provide at least one of
the following:
Protection from HIV infection: acquisition or
sterilizing immunity
Protection from disease: if infected, low HIV
RNA “set point”, preservation of CD4 cells, long
term non-progressor (LTNP)-like clinical state
Proof of Concept (POC) Efficacy Study
• Design
– Randomized, double-blind, placebo-controlled
– Men and women at high risk of acquiring HIV infection
– HIV diagnostic test every 6 mos. (~ 3 years follow-up)
– Event-driven trial- follow until 50th HIV infection
• Co-Primary Endpoints
– HIV infection
– Viral load set-point (~ 3 months after diagnosis of HIV
infection)
• Secondary/exploratory endpoints in HIV infected subjects
– Viral load at 6-18 months
– Rate of CD4 decline
– Time to initiation of ART
POC Efficacy Study, continued
• Null Hypothesis: Vaccine is same as Placebo
Same HIV infection rates (VES = 0%) and
Same distribution of viral load among infected
subjects
• Alternative Hypothesis: Vaccine is better than Placebo
Lower HIV infection rate (VES > 0%) and/or
Lower viral load for infected subjects in vaccine arm
• Proof of Concept: Reject above composite null
hypothesis with at least 95% confidence
– 90% power to detect VES > 60% if no viral load effect
– 90% power to detect a 0.7log10 viral load effect if VES = 0%
Prototype Phase III Trial
Vax004 Phase III Trial (North
America/Netherlands 1998-2003)
• 5,403 HIV-negative MSM and women randomized to
vaccine or placebo
– Immunizations: 0, 1, 6, 12, 18, 24, 30
months
– HIV tests:
0, 6, 12, 18, 24, 30, 36 months
– Antibody responses measured at immunization visits
and 2-weeks post-immunizations
• HIV seroconverters monitored for:
– Progression biomarkers (viral load, CD4 count)
– HIV genetic sequences
– immune responses
– Initiation of ART
– HIV-related clinical events
• Post infection diagnosis visit schedule:
0, 1/2, 1, 2, 3, 4, 5, 6, 12, 18, 24 months
Vax004 Phase III Trial (North
America/Netherlands 1998-2003)
Question 1: Infection Endpoint
• Diagnosis of HIV infection is the standard primary
endpoint of an efficacy trial
• How evaluate the vaccine efficacy to prevent HIV
infection (VES)?
– How define the endpoint HIV infection?
– How make unbiased assessment of VES?
– How evaluate durability of VES?
 Concern of waning efficacy due to loss of immunological memory
Question 2: Post-infection Endpoints
• What effects on which post-infection endpoints
indicate vaccine effectiveness on
progression/infectiousness?
– Which clinical outcomes and how much follow-up required to
evaluate VEP directly?
– What can be learned about VEP in Phase IIb/III trials versus
what should be left for larger-scale post-licensure
epidemiological studies?
– Which surrogate endpoints, what duration to study them, and
how reliable are they for informing on VEP/VEI?
– When should community randomized trials be implemented?
Question 2: Post-infection Endpoints,
Continued
• How achieve valid analysis of vaccine effects on postinfection endpoints?
• What impact does the infecting HIV
genotype/phenotype have on post-infection vaccine
effects?
• What T cell responses to which HIV epitopes durably
control viremia, and how to identify T cell escape
mutations?
• Are there immunological correlates of protection
against post-infection endpoints?
• How does vaccination impact the effectiveness of
ARTs?
Question 3: Correlates of Protective
Immunity
• How identify immune responses that correlate with
protection against HIV infection?
– What is the optimal sampling design?
 Case-cohort?
 Nested matched case-control?
 Which time-points to assay samples?
– How distinguish between “mere correlates” of HIV infection
rate versus “causal surrogates” of protection
Question 4: Strain-Specific VES
• How evaluate the dependency of VES on
genotypic/phenotypic properties of the exposing HIVs?
– How summarize immunologically-relevant distance between an
infecting HIV strain and the HIV strain(s) represented in the
immunogen?
– How estimate relationship between VES and a given distance?
– How identify particular mutations in the HIV genome that may
have caused vaccine failure?
1: Assess VES
• Primary endpoint typically is clinically significant infection
– E.g., HIV antibody positive by ELISA + Western Blot
– Historically in non-HIV vaccine trials, primary endpoint is symptomatic
disease and detection of the infectious pathogen
• Under randomization and blinding VES can be validly assessed by
comparing the rates of HIV infection between the study arms
– Secondary analyses incorporate data on risk factors including risk
behavioral data
• Durability of VES can be assessed by methods that estimate VES
over time
– Kaplan-Meier analysis of time to HIV infection
– Estimate VES(t) as one minus the hazard ratio (vaccine/placebo) over
time
1: Assess VES in Vax004
Standard
Kaplan-Meier
Analysis
More sophisticated
Methods that
Accounted for
Estimated
Contact rates gave
Similar results
[Ira Longini]
1. Assess VES(t) in Vax004 as one minus
hazard ratio (vaccine/placebo)
VES(t) =
1 – HR
No evidence
That VES differs
From 0 at
Any t
2. Approach to Assessing Vaccine Efficacy on
Progression/Infectiousness (VEP/VEI)
• Clinical endpoints
– HIV-related conditions
 Vax004 used CDC 1993 definition
– WHO stage 2/3
• Surrogate endpoints for AIDS progression and
transmission to others
– RNA viral load
– CD4 count
2. Rationale for Viral Load Endpoint: Risk of Progression
to AIDS by 9 years in the MACS of ART-naïve
Homosexual Men with CD4 counts < 350 cells/mm*
90
80
70
60
50
Percent
40
30
20
10
0
< 1,500
1,5017,000
7,00120,000
20,001- > 55,000
55,000
Viral Load (copies/ml)
*Statistics
obtained from Table 5 of
DHHS Guidelines for the use of ART,
http://www.aidsinfo.nih.gov/guidelines/
2. Rationale for Viral Load Endpoint: Probability of
Transmission per Coital Act in HIV-Discordant Couples in
Rakai, Uganda*
25
20
Probability
x10,000
15
10
5
0
< 1,700
1,70012,499
12,50038,500
> 38,500
Viral Load (copies/ml)
*Statistics obtained from Table 2 of Gray
et al. (Lancet 2001; 357:1149-1153)
2. Challenges with Surrogate Endpoints
• Vaccine effects on surrogate endpoints may not predict
vaccine effects on clinical endpoints
– Common pitfall in clinical trials in many disease areas
• Use of ARTs obscures direct assessment of mid-to-long
range vaccine effects on viral load/CD4
• The assessment of the vaccine effect on post-infection
endpoints is susceptible to selection bias
Hypothetical Example of Selection Bias
Immune System
Vaccine
Strong
Weak
Placebo
Strong
Weak
Viral Load
-5
3
5
Number Infected
0
10
10
10
For vaccine group, mean log10 viral load = 5
For placebo group, mean log10 viral load = 4
Comparing viral loads between infected vaccine and placebo recipients
would suggest that vaccination increases viral load. But in truth the
vaccine has no effect on viral load.
Therefore, the straightforward, standard analysis is misleading
2. Post-Infection Surrogate Endpoints
• Categories of surrogate endpoints:
– Early: 1-3 months post infection diagnosis
Measured in all seroconverters prior to ART initiation
– Mid: 6-24 months
Biomarkers affected by ART initiation
Vaccine effects may trigger provisional licensure
– Late: > 24 months
Confirm benefit suggested by early/mid effects
Clinical and CD4 endpoints key
Assess vaccine effects on clinical endpoints
regardless of ART use
2. Set-Point Viral Load Endpoint
• Initial pre-ART viral load
– E.g., the average of pre-ART viral loads measured
13 months post infection diagnosis
– Surrogate vaccine efficacy parameter:
VEVL = Mean(VL;placebo) - Mean(VL;vaccine)
2. Pre-ART Viral Loads in Vax004
Early viral
Load is similar
In vaccine and
Placebo arms
2. Vax004: Sensitivity Analysis of the Average
Causal Effect (ACE) on Set-Point Viral Load
Assessments
of vaccine
effects on
endpoints
only measured
in infected
subjects are
susceptible to
post-randomization
selection biasEmploy causal
Inference
methods
2. Early Post-Infection Surrogate Endpoints
• Limitation of early surrogate endpoints: Do not measure
the durability of vaccine effects
• Initial vaccine-induced control of viremia may be lost
due to immune escape
– E.g., CTL or T helper escape; Barouch et al. (Nature,
2002)
• Early surrogate endpoints insufficient for making
reliable predictions of VEP and VEI
Late surrogate endpoints must also be studied
Loss of Viral Control in a Rhesus Monkey
(Barouch et al., 2002)
a. Viral load
b. CD4 count
c. T cell response
to protective epitope
d. T cell response to
mutant epitope
e. T cell response to
mutant epitope
f. Antibody response
2. Bias in Assessing Late Viral Load, Due to
Dependent Censoring by ART Initiation
2. Approaches to Assessing Later Viral
Load Endpoint that Avoid bias
• Analyze a composite endpoint
– First event of viral failure > x cps/ml or ART initiation
– Standard survival analysis methods valid
• Exclude viral loads measured after ART, and use
specialized statistical methods to adjust for the
dependent censoring
– Linear mixed effects models, which include covariates that
predict ART initiation
 Can accommodate the detection limits of the viral load assay [Jim
Hughes, 1999, Biometrics]
– Generalized estimating equations (GEE) models with multiple
imputation
2. Vax004: VE to Prevent the Composite
Endpoint for 4 different failure thresholds
Vaccine efficacy
Near zero
For all viral
Failure thresholds
2. Vax004: VE to Prevent ART Initiation
2. Vax004: Interpret the Analysis Relative to
Treatment Guidelines
• U.S. 2002 DHHS Guidelines
– Start ART when viral load > 55,000 cps/ml, CD4 < 350
cells/mm3, or HIV-related clinical event
• Interpret composite endpoint analysis with X = 10,000
cps/ml
– 279 total composite endpoints
– 208 (75%) due to viral failure > 10,000 before ART
– 71 (25%) due to ART before viral failure
 61 of these 71 had CD4 > 350- started ART prematurely
 These 61 endpoints are possible noise that could attenuate a real
vaccine effect
 Excluding these endpoints, composite endpoint rates still
comparable among vaccine and placebo arms
[136/225 (60%) events for vaccine vs 82/122 (67%) events for
placebo]
2. Vax004: Simultaneous Confidence Bands on
VE to Prevent the Composite Endpoint
Vaccine efficacy
Near zero
For all viral
Failure thresholds
2. Vax004: No Vaccine Efficacy on
Infection or Composite Endpoint
3. Vax004 Immune Response Data
• 8 assays for measuring antibody responses to the MN and GNE8
strains of HIV:
– ELISA for antibodies to gp120, V2, V3; blocking of binding to CD4
– MN neutralization
• Specimens collected:
– Month 0, 1, 6, 12, 18, 24, 30 (troughs)
– Month 0.5, 1.5, 6.5, 12.5, 18.5, 24.5, 30.5 (peaks)
• Specimens assayed:
– All infected vaccinees (n=239, last sample prior to infection)
– 5% of uninfected vaccinees (n=163, all time points)
3. Analysis of Immune Responses
• Study the association of levels of antibody response to
vaccine with the rate of HIV infection
– Cox model with case-cohort sampling design can be
used to estimate relative risks of infection by level of
antibody responses
– A nested matched case-control design would provide
similar statistical power
Vax004 Estimates of VEs(Q1), VEs(Q2), VEs(Q3), VEs(Q4)
MN CD4
VEs
GNE8 CD4
MN V2
GNE8 V2
100%
100%
100%
100%
75%
75%
75%
75%
50%
50%
50%
50%
25%
25%
-
0%
0%
-25%
-25%
-50%
-50%
-75%
-
-100%
Q1
Q2
Q3
Q4
- - ---
25%
25%
0%
0%
-25%
-25%
-
-50%
-50%
-
-75%
-75%
-75%
-100%
-100%
-100%
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
risk of infection for vaccinees with Quartile x antibody response
VEs(Qx) = 1 - risk of infection for placebo group
3. How Distinguish a ‘Mere Correlate’
from a ‘Causal Surrogate’?
•
For MN CD4 and GNE8 CD4 responses, vaccinees with
lowest antibody responses had a higher rate of HIV
infection than placebos, and vaccinees with highest
responses had a lower rate
•
Two possible explanations:
1. Response marked intrinsic susceptibility to HIV
2. Low (high) response caused a higher (lower) infection
probability
3. How Distinguish a ‘Mere Correlate’
from a ‘Causal Surrogate’?
•
To try to discern between 1. And 2. would like to
compute VEs(Qx) = 1 -
risk of infection for vaccinees with Quartile x antibody response
risk of infection for placebos who would have had Quar x ab response if vaccinated
–
•
This would be a causal inference that would answer the
question
 VEs(Qx) constant over quartiles- mere correlation
 VE(Qx) increasing in Qx- causation
Missing data methods needed to be able to estimate
the causal estimand Ves(Qx)
–
Approaches for doing this
 Vaccinate placebo recipients at study close-out
 Measure baseline variables that do not directly impact HIV
susceptibility and that predict the immune response to the HIV
vaccine
3. Using a Baseline Predictor to impute
the immune response for Placebos
4. Assess Strain-Specific VES
• In trial participants who become HIV-1 infected, the
infecting viruses are isolated and sequenced
• If the vaccine partially protects against HIV infection
(VES > 0%), then expect that VES is higher against
viruses genetically closer to the vaccine strain
• Develop methods for studying
– VES as a function of genetic distance
– VES as a function of amino acid patterns (high dimensional data)
4. Neutralizing Face Core Distance
Wyatt et al. (1998,
Nature)
4. Vax004: Estimated VES as a Function
of HIV Genetic Distance to the Vaccine
Genetic distance
= Hamming
Distance of
Neutralizing
Face Core amino
Acids
4. Genome Scanning to Detect Amino
Acid Signatures Impacting VES
4. Vax004: No Significant Signatures in
the HIV gp120 Gene
This null
result is
expected
since
VES ~ 0%