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On the analysis of viral load endpoints in HIV vaccine trials PowerPoint PPT Presentation

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 l.jpg

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


Possible vaccine effects l.jpg

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

Barcelona , 10 July 2002


Question l.jpg

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

Barcelona , 10 July 2002


Challenges l.jpg

2. Accounting for ART

Challenges

1. Viral load is a surrogate marker

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

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Intent to treat l.jpg

+ 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

Barcelona , 10 July 2002


Condition on infection l.jpg

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

Barcelona , 10 July 2002


Selection bias example i l.jpg

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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Selection bias example ii l.jpg

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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Slide9 l.jpg

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

Goal

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Testing for ve on vl l.jpg

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

Barcelona , 10 July 2002


Selection bias model l.jpg

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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Slide12 l.jpg

Barcelona , 10 July 2002


Testing for ve on vl13 l.jpg

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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Simulated efficacy trial l.jpg

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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Slide15 l.jpg

Simulation Results - Power

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Simulation results power l.jpg

Intent-to-treat vs Conditional

Test for difference in mean VL using bootstrap

VES=0

Simulation results - Power

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Conclusions l.jpg

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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Other applications l.jpg

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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References l.jpg

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

Barcelona , 10 July 2002


Acknowledgments l.jpg

Steve Self, FHCRC, Seattle

Antje Hoering, Insightful Corp, Seattle

Peter Gilbert, FHCRC, Seattle

M. Elizabeth Halloran, Emory Univeristy, Atlanta

NIAID

Acknowledgments

Barcelona , 10 July 2002


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