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Statistical Design and Analysis for Immune Correlates Assessment: Basic Concepts and RV144 Illustration

Statistical Design and Analysis for Immune Correlates Assessment: Basic Concepts and RV144 Illustration. Yunda Huang 1 , Holly Janes 1, 2 , Peter Gilbert 1, 2 1 Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center

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Statistical Design and Analysis for Immune Correlates Assessment: Basic Concepts and RV144 Illustration

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  1. Statistical Design and Analysis for Immune Correlates Assessment: Basic Concepts and RV144 Illustration Yunda Huang1, Holly Janes1, 2, Peter Gilbert1, 2 1 Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center 2Department of Biostatistics, University of Washington Seattle, Washington, USA IAS 2012 Correlates Workshop, Washington DC, USA

  2. Reasons to Be a Statistician • … • … • … • … • No one knows what we do so we are always right.

  3. Outline • Definitions • Correlates Study and Sampling Design • Example: RV144 Immune Correlates Study • Strategies to Evaluate Immune Correlates • Summary

  4. In the context of preventive HIV vaccine clinical trials • Rate of HIV infection: frequency of new HIV infections during a specified time frame – can be measured in vaccine (Rv) and placebo (Rp)groups separately • Vaccine efficacy: proportion of infections prevented by vaccine relative to placebo (1- Rv/ Rp) – need to know the rate of infection in both vaccine and placebo groups • Correlates of risk (CoR): Immune markers statistically correlated with the rate of HIV infection in the vaccine group (Qin & Gilbert et al., JID, 2007) • Correlates of protection (CoP): Immune markers statistically correlated with vaccine efficacy in the vaccine and placebo groups (Qin & Gilbert et al., JID, 2007; Plotkin & Gilbert, CID, 2012)

  5. Correlates of Risk (CoR) and Correlates of Protection (CoP) • Vital for vaccine development • Choice of antigens included in vaccines • Bridge from previously collected protection data • Surrogate for efficacy evaluation • Population and individual level immunity measure • Easily being confused and used inter-changeably • Three facts: • A CoR may not be a CoP, but could be • A CoP must be a CoR • Not all CoRsor CoPs are created equal

  6. Fact #1a: A CoR may not be a CoP • It is a CoR – because the levels of the immune response are correlated with the rate of infection in the vaccine group • Is it a CoP? Rate of HIV Infection Immune Response

  7. Fact #1a: A CoR may not be a CoP • It is a CoR • It is not a CoP– if, in the same way, the rate of infection is correlated with the immune response (had it been measured) in the placebo as well Rate of HIV Infection Immune Response

  8. Fact #1a: A CoR may not be a CoP • It is a CoR • It is not a CoP • The immune responses from this biomarker are not predictive of VE (≠ CoP), although overall VE=40% Vaccine Efficacy (%) Rate of HIV Infection Immune Response

  9. Fact #1a: A CoR may not be a CoP • It is a CoR • It is not a CoP • Why: • Those individuals who could mount a strong immune response are better able to ward off infection “on their own” with no impact of the vaccine-induced immune responses of this marker • “On their own”: It may mark susceptibility to infection independent of Vaccination, e.g., risk behavior or host genetics Vaccine Efficacy (%) Rate of HIV Infection Immune Response

  10. Fact #1b: A CoR could be a (perfect) CoP • It is a CoR • Is it a CoP? Rate of HIV Infection Immune Response

  11. Fact #1b: A CoR could be a (perfect) CoP • It is a CoR • It is a CoP: those individuals who could mount a strong immune response are better able to remain uninfected, differently in vaccine and placebo recipients if assigned vaccine • And, these immune responses (=CoR) are also predictive of vaccine efficacy (=CoP) Vaccine Efficacy (%) Rate of HIV Infection Immune Response

  12. Fact #2: A CoP must be a CoR • It’s equivalent to show: If not a CoR, then not a CoP • Immune responses from vaccinees are not predictive of rate of infection -- not a CoR Rate of HIV Infection Immune Response

  13. Fact #2: A CoP must be a CoR • It’s equivalent to show: If not a CoR, then not a CoP • Immune responses from placebos will not be predictive of rate of infection Rate of HIV Infection Immune Response

  14. Fact #2: A CoP must be a CoR • It’s equivalent to show: If not a CoR, then not a CoP • Immune responses will not be predictive of vaccine efficacy Vaccine Efficacy (%) Rate of HIV Infection Immune Response

  15. Fact #3a: Not all CoRs are created equal Rate of HIV Infection Immune Response

  16. Fact #3a: Not all CoRs are created equal Rate of HIV Infection Immune Response Immune Response

  17. Fact #3b: Not all CoPs are created equal Vaccine Efficacy (%) Immune Response

  18. Fact #3b: Not all CoPs are created equal Vaccine Efficacy (%) Immune Response

  19. Fact #3b: Not all CoPs are created equal Vaccine Efficacy (%) Immune Response

  20. Outline • Definitions • A CoR may not be a CoP, but could be • A CoP must be a CoR • Not all CoRs or CoPs are created equal • Correlates Study and Sampling design • Example: RV144 Immune Correlates study • Strategies to Evaluate Immune Correlates • Summary

  21. Time-dependent and Time-independent CoR • Time-independent immune correlates analysis: discover correlates at a specific time point • e.g. immune responses 2 weeks after the last vaccination • Peak immune response time point close to baseline • Informative and practical • Time-dependent immune correlates analysis: discover correlates whose levels may change over time • e.g., most recent immune responses before diagnosis of infection • Immune response near the time of exposure with the acute risk of infection • Informative about the mechanism of protection

  22. CoR Study Design • HIV vaccine-induced immune responses are only assessed in vaccinees • Statistical power of an immune correlates study is driven by the number of HIV infections among vaccinees • For a given total, the number of vaccinee infections depend on the vaccine efficacy: the higher the VE, the smaller number of infections from the vaccine group • The smaller # infections is, the stronger the correlation between the immune response and the rate of infections needs to be, in order to have the same statistical power for CoR detection

  23. CoR Study Sampling design • With unlimited resources, we could measure the post-vaccination immune responses from every vaccinees • Several cohort study designs have been developed to save resources with minimal loss of power after adjusting for the sampling design • Case-cohort (traditional): controls are sampled without regard to infection time as part of a subcohort • Case-control: controls are sampled after ascertainments of cases

  24. CoR Study Sampling Design: Case-cohort • Traditionally, controls are sampled without regard to failure times as part of a subcohort • Sampling can be done a priori without regard to case status or time • All cases are included whether they occur in the subcohort or not; controls are included only if in the subcohort • Estimate population level immune responses • Could select controls for multiple outcomes • Lately, some case-cohort designs are also outcome-dependent

  25. CoR Study Sampling Design: Case-control • Controls are sampled after ascertainments of cases • Individual matching, frequency matching or stratification to sample appropriate controls for cases • Matching addresses issues of confounding in the DESIGN stage of a study as opposed to the ANALYSIS phase, providing a more efficient analysis (reduction in standard errors of estimates) • Matching on non-confounders may lose efficiency compared to the non-matched case-control approach

  26. Analysis Method • Standard Cox or logistic regression models if data on the full cohort were available • Modified Cox or logistic regression models if sub-sampling is done to account for the sampling design • Breslow and Holubkov (1997, Biometrika) • Borgan et al. estimator II (2000, Lifetime Data Analysis)

  27. Outline • Definitions • Correlates Study and sampling design • Number of infections drives the power of the study • Sampling designs with corresponding analysis methods • Example: RV144 immune correlates study • Strategies to evaluate different immune correlates • Summary

  28. RV144 Thai Trial Primary Results

  29. RV144 Thai Trial

  30. Impetus for the Correlates Study:Evidence for Partial Vaccine Efficacy C. Modified Intention-to-Treat Analysis* 1.0 0.9 0.8 Placebo 0.7 0.6 Vaccine Probability of HIV Infection (%) 0.5 0.4 0.3 0.2 0.1 0.0 *N=16,395 assessed; 51 Vaccine, 74 Placebo HIV-1 infected Estimated VE = 31% [95% CI 1−51%], p=0.04 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Years Objective: To carry out an immune correlates analysis to begin to identify how the vaccine might work

  31. RV144 Correlates of Risk Results

  32. What the RV144 Correlates Study Assessed • The analysis sought to discover Correlates of Risk (CoR): Immune response variables that predict whether vaccinees become HIV-1 infected • Thus, the study is designed to generate hypotheses that certain immune responses are Correlates of Protection (CoP) that would need validation in future research

  33. RV144: Two Tiers of Studies • Pilot immunogenicity studies • Multiple immunology labs to perform assays on sample-sets from HIV uninfected RV144 participants • Conducted standardized comparative analyses of all candidate assays, to down-select the best performing assays and to optimize the immune variables to study as correlates • Case-control study • Assessed the selected immune variables as correlates of infection risk

  34. RV144 Example Pilot Data: gp70-V1V2 Binding Antibodies (ELISA)

  35. RV144: Criteria for Advancing Assaysto the Case-Control Study

  36. RV144 Case-Control Analysis: Two Tiers • Primary Analysis: 6 priority immune response variables • Secondary Analysis: All other immune response variables that passed pilot study criteria • This division maximizes statistical power for the priority immune variables while allowing a broader exploratory analysis

  37. Down-Selected Primary Immune Variables

  38. RV144 Case-Control Study • Time-independent CoR: What are the immunologic measurements at a fixed time-point (wk26) in vaccinees that predict HIV-1 infection over a 3 year follow-up? • Sampling design: Balanced stratified random sampling for vaccinees− 5:1 (control:case) ratio within each of the following covariate strata Gender × Number of vaccinations × Per-protocol status • 41 infected vaccinees (all available) • 205 uninfected vaccinees (5:1 stratified random sample) • 40 placebo recipients (simple random sample) • Outcome-dependent 2-phase sampling case-cohort study

  39. Why 5:1 Sampling? Only a Small Power Loss Moving from 5:1 to 10:1

  40. RV144 Immune Correlates Study Main Result* *Multivariate logistic regression (quantitative variables) adjusted for gender, baseline behavioral risk (low, medium, high) **1-Qvalue ≈ estimated prob. that the immune variable correlates with infection rate • All 6 variables together in multivariate analysis: p=0.08 • The 2 correlates in multivariate analysis: p=0.01

  41. V1V2-gp70 Scaffold ELISA High Medium Low

  42. Cumulative Infection Rates with V1V2-gp70 Scaffold Assay Low/Medium V1V2 HighV1V2 Estimated Relative Risk High vs. Low = 0.29

  43. Plasma IgA Binding To Envelope Panel High Medium Low

  44. Cumulative Infection Rateswith IgAEnv Binding Assay High EnvIgA Low/Medium Env IgA Estimated Relative Risk High vs. Low = 1.89

  45. Sieve Analysis is an Integral Part of Immune Correlates Assessment • The correlates analysis showed V1V2 Abs predicted infection in the vaccine group only • Sieve analysis examines evidence for a difference in the sequences of viruses infecting vaccinees versus placebo recipients • Observed differences can be attributed to the vaccine in a randomized trial • Detection of a ‘sieve effect’ may suggest that the vaccine blocks infection with some types of exposing HIVs • If certain epitope-specific Ab responses are protective, then would expect to see a relative absence of these specific epitopes in sequences of infected vaccinees compared to infected placebo recipients • Found additional evidence for vaccine pressure on the V2 mid-loop region

  46. What it Could Mean (Most Useful for Vaccine Development) • The gp70-V1V2 antibody CoR would be most useful for vaccine development if it strongly predicted VE (i.e., was a good CoP)

  47. But, It Could Also Mean • The gp70-V1V2 antibody CoR does not predicted VE (≠CoP)

  48. Outline • Definitions • Correlates Study and Sampling design • Example: RV144 Immune Correlates Study • Case-control study • Evidence for two correlates of infection risk in vaccinees • IgG antibodies that bind to scaffolded-V1V2 recombinant protein correlated inversely with infection rate • Plasma IgA antibodies correlated directly with infection rate • Strategies to evaluate different immune correlates • Summary

  49. Strategies to Assess CoRs as VE-Predictors (CoPs) and as Mechanisms of Protection • Collect the requisite data for correcting the CoR analysis for potential exposure confounding (e.g., risk behavior, host genetics) • Collect the requisite data for directly assessing the ability of a CoR to predict VE (more on next few slides) • Conduct sieve analysis of HIV sequences to assess whether the vaccine applied pressure on the HIV Envtarget(s) specific to the immune correlate • Collaborate with other groups (e.g, CHAVI, CAVD, VRC) conducting experiments (e.g., in non-human primates) testing hypotheses about the CoRs

  50. Once a positive CoR is discovered in vaccinees • Collect the requisite data for directly assessing the ability of a CoR to predict VE • To assess the relationship between VE and an immune marker (i.e., CoP), we need to know the level of the immune marker for both vaccine and placebo recipients – fill in all the blanks

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