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On the analysis of viral load endpoints in HIV vaccine trials Michael Hudgens, Antje Hoering and Steve Self Fred Hutchinson Cancer Research Center Seattle, WA USA VE S effect on susceptibility Primary objective of preventive efficacy trials Possible Vaccine Effects

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On the analysis of viral load endpoints in HIV vaccine trials

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On the analysis of viral load endpoints in HIV vaccine trials

Michael Hudgens, Antje Hoering

and Steve Self

Fred Hutchinson Cancer Research Center Seattle, WA USA


VESeffect on susceptibility

Primary objective of preventive efficacy trials

Possible Vaccine Effects

  • VEP effect on disease progression

    • Requires long term follow-up of infected participants

  • VEIeffect on infectiousness

    • Requires partner study or community randomization

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How do we draw inference regarding VEP or VEI within the framework of a classic efficacy trial design?

Question

  • Consider vaccine effects on surrogate markers of disease progression or secondary transmission

  • E.g., viral load, CD4+ count

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2. Accounting for ART

Challenges

1. Viral load is a surrogate marker

3. Intent-to-treat vs. condition on infection

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+ Preserves randomization

Intent-To-Treat

  • Compare VL in all participants according to randomization assignment

- Requires assigning VL to uninfected participants

- Potential loss of power

- Does not separate vaccine effects

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Compare VL in infected participants only according to randomization assignment

Condition on Infection

  • Subject to selection bias since comparing two groups that are defined by a post - randomization event, infection

  • Most relevant selective mechanism: VES

    • Participant immune system

    • Infecting viral strain

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Suppose vaccine protects recipients with stronger immune systems

Selection bias – Example I

  • Infected vaccinees have weaker immune systems on average than infected controls

  • Vaccine may appear to enhance VL

    due solely to selection bias

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Suppose vaccine protects recipients against relatively innocuous strains

Selection bias – Example II

  • Vaccinees infected by more virulent strains on average than infected controls

  • Vaccine appears to enhanceVL

    due solely to selection bias

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Test for a vaccine effect on viral load beyond possible selection bias

Goal

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Define difference in mean VL:

=mean VLV – mean VLC

Testing for VE on VL

  • Due to selection bias replace

    H0:   0

    by

    H0 :  VL

    where threshold VL is determined by amount

    of selection

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Assume:

Integrity of randomization, blinding

VES0

Vaccine has no effect on VL (null hypothesis)

Selection bias - model

  • Then:

    • VL distribution for infected vaccinees can be represented as a rescaled subdistribution of the VL distribution for infected controls

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Assume extreme selection bias model

Testing for VE on VL

  • Then testing

    H0 : VL

    is simply testing for a difference in means greater than VL between two groups

  • E.g., t-test

  • Bootstrap procedure employed to assess significance

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VES: 0%, 30%, 50%, 80%

Simulated Efficacy Trial

  • Randomized, placebo control: NV=NC=1000

  • Number of infected controls: nC=90

  • Three scenarios

    • H0: =VL

    • HA: =VL + 1/3

    • HA: =VL + 1/2

  • VL distribution:=4.5, 2=0.4

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Simulation Results - Power

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Intent-to-treat vs Conditional

Test for difference in mean VL using bootstrap

VES=0

Simulation results - Power

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Comparing post-infection endpoints (VL, CD4+) in infected vaccinees vs infected controls subject to selection bias

Conclusions

  • Statistical methods have been developed to test for vaccine effects on post-infection endpoints beyond possible selection bias

  • Adequate power remains to detect clinically meaningful effects

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Dichotomous outcomes

Disease/death

Secondary transmission

Other Applications

  • Monkey studies

  • Mother to child transmission studies

    • Condition on survival of the child beyond birth

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Hudgens, Hoering, and Self. On the analysis of viral load endpoints in HIV vaccine trials. Statistics in Medicine, In press.

Gilbert and Bosch. Sensitivity analysis for the assessment of vaccine effects on viral load in HIV vaccine trials. Submitted.

References

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Steve Self, FHCRC, Seattle

Antje Hoering, Insightful Corp, Seattle

Peter Gilbert, FHCRC, Seattle

M. Elizabeth Halloran, Emory Univeristy, Atlanta

NIAID

Acknowledgments

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