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Statistical Science Issues in Preventive HIV Vaccine Efficacy Trials: Part II

Statistical Science Issues in Preventive HIV Vaccine Efficacy Trials: Part II. Outline. Efficacy Trial Objectives Phase IIb vs Phase III Scientific questions Approaches to the questions HIV infection endpoint Post-infection endpoints Correlates of protective immunity

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Statistical Science Issues in Preventive HIV Vaccine Efficacy Trials: Part II

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  1. Statistical Science Issues in Preventive HIV Vaccine Efficacy Trials: Part II

  2. Outline • 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

  3. 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

  4. 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

  5. Trial Objectives: Correlates of Protective Immunity • If protection observed, evaluate correlation of vaccine-induced 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

  6. 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

  7. Phase IIb vs Phase III Efficacy Trials • Phase IIb~30-60 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 strain-specific efficacy • Phase III ~125-200 infections in placebo arm • 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)

  8. 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

  9. 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

  10. POC Efficacy Study, continued • NullHypothesis: 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%

  11. Prototype Phase III Trial

  12. 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

  13. Vax004 Phase III Trial (North America/Netherlands 1998-2003)

  14. 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

  15. 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?

  16. Question 2: Post-infection Endpoints, Continued • How achieve valid analysis of vaccine effects on post-infection 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?

  17. 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

  18. 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?

  19. 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

  20. 1: Assess VES in Vax004 Standard Kaplan-Meier Analysis More sophisticated Methods that Accounted for Estimated Contact rates gave Similar results [Ira Longini]

  21. 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

  22. 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

  23. 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* *Statistics obtained from Table 5 of DHHS Guidelines for the use of ART, http://www.aidsinfo.nih.gov/guidelines/

  24. 2. Rationale for Viral Load Endpoint: Probability of Transmission per Coital Act in HIV-Discordant Couples in Rakai, Uganda* *Statistics obtained from Table 2 of Gray et al. (Lancet 2001; 357:1149-1153)

  25. 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

  26. Hypothetical Example of Selection Bias Immune SystemViral LoadNumber Infected Vaccine Strong -- 0 Weak 5 10 Placebo Strong 3 10 Weak 5 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

  27. 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

  28. 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)

  29. 2. Pre-ART Viral Loads in Vax004 Early viral Load is similar In vaccine and Placebo arms

  30. 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-rand- • omization • selection bias- • Employ causal • Inference • methods

  31. 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

  32. 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

  33. 2. Bias in Assessing Late Viral Load, Due to Dependent Censoring by ART Initiation

  34. 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

  35. 2. Vax004: VE to Prevent the Composite Endpoint for 4 different failure thresholds Vaccine efficacy Near zero For all viral Failure thresholds

  36. 2. Vax004: VE to Prevent ART Initiation

  37. 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]

  38. 2. Vax004: Simultaneous Confidence Bands on VE to Prevent the Composite Endpoint Vaccine efficacy Near zero For all viral Failure thresholds

  39. 2. Vax004: No Vaccine Efficacy on Infection or Composite Endpoint

  40. 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)

  41. 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

  42. Vax004 Estimates of VEs(Q1), VEs(Q2), VEs(Q3), VEs(Q4) VEs risk of infection for vaccinees with Quartile x antibody response risk of infection for placebo group VEs(Qx) = 1 -

  43. 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: • Response marked intrinsic susceptibility to HIV • Low (high) response caused a higher (lower) infection probability

  44. 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

  45. 3. Using a Baseline Predictor to impute the immune response for Placebos

  46. 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)

  47. 4. Neutralizing Face Core Distance Wyatt et al. (1998, Nature)

  48. 4. Vax004: Estimated VES as a Function of HIV Genetic Distance to the Vaccine Genetic distance = Hamming Distance of Neutralizing Face Core amino Acids

  49. 4. Genome Scanning to Detect Amino Acid Signatures Impacting VES

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