Statistical methodology for evaluating a cell mediated immunity based hiv vaccine
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Statistical Methodology for Evaluating a Cell Mediated Immunity-Based HIV Vaccine. Devan V. Mehrotra* and Xiaoming Li Merck Research Laboratories, Blue Bell, PA *e-mail: [email protected] Biostat 578A Lecture 4

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Statistical Methodology for Evaluating a Cell Mediated Immunity-Based HIV Vaccine

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Statistical methodology for evaluating a cell mediated immunity based hiv vaccine

Statistical Methodology for Evaluating a Cell Mediated Immunity-Based HIV Vaccine

Devan V. Mehrotra* and Xiaoming Li

Merck Research Laboratories, Blue Bell, PA

*e-mail: [email protected]

Biostat 578A Lecture 4

Adapted from Devan’s presentation at the ASA/Northeastern Illinois Chapter Meeting

October 14, 2004


Outline

Outline

  • Science behind the numbers

  • Merck’s HIV vaccine project

  • Proof of concept (POC) efficacy study

  • Statistical methods

  • Simulation study

  • Concluding remarks


Worldwide distribution of hiv 1 clades subtypes

Worldwide Distribution of HIV-1 Clades (Subtypes)*

A, B, AB, Other

B, Other

G

B

A

F

B, Other

B

B, AE

B, BC

B, G, Other

G, Other

B, O

C

C

B, Other

O

A, Other

A

AE, B, Other

B, AE, Other

AG

All

G, Other

A,C,D

B, F, Other

Legend

C

B

B

B dominant + Another

C, Other

C

B, F

O

B, AE

A

B, C

All

Other

Note:*Dominant clades are bolded above; All regions have multiple clades in their populations


T cell recognition of infected cells

T Cell Recognition of Infected Cells


Statistical methodology for evaluating a cell mediated immunity based hiv vaccine

HIV Infection: CD4 cell count and Viral Load


Merck s hiv vaccine project

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

Proof of Concept (POC) Efficacy Study

  • Design

    -Randomized, double-blind, placebo-controlled

    - Subjects at high risk of acquiring HIV infection

    - HIV diagnostic test every 6 mos. (~ 3 yrs. f/up)

  • Co-Primary Endpoints

    -HIV infection status (infected/uninfected)

    -Viral load set-point (vRNA at ~ 3 months after diagnosis of HIV infection)

  • Secondary/exploratory endpoints: vRNA at 6-18 months, rate of CD4 decline, time to initiation of antiretroviral therapy, etc., for infected subjects


Poc efficacy study continued

POC Efficacy Study (continued)

  • Vaccine Efficacy (VE) =

  • Null Hypothesis: Vaccine is same as Placebo

    Same HIV infection rates (VE = 0) and

    Same distribution of viral load among infected subjs.

  • Alternative Hypothesis: Vaccine is better than Placebo

    Lower HIV infection rate (VE > 0) and/or

    Lower viral load for infected subjects who got vaccine

  • Proof of Concept: reject above composite null hypothesis with at least 95% confidence


Notation for statistical methodology

Notation for Statistical Methodology


Notation cont d

Notation (cont’d)


Notation cont d1

Notation (cont’d)


Competing methods for establishing poc

Competing Methods for Establishing POC


Optimal weights for viral load component of composite test w 2 under different scenarios

Optimal Weights for Viral Load Component of Composite Test (w2) under Different Scenarios


Methods for establishing poc cont d

Methods for Establishing POC (cont’d)


Methods for establishing poc cont d1

Methods for Establishing POC (cont’d)


Illustration of simes weighted simes fisher s weighted fisher s methods hypothetical examples

Illustration of Simes, Weighted-Simes, Fisher’s, Weighted-Fisher’s Methods (Hypothetical Examples)

Note: w1 = .14, w2 = .86 for weighted-Simes’ and weighted-Fisher’s methods


Critical boundaries simes weighted simes fisher s weighted fisher s

Critical Boundaries: Simes, Weighted-Simes, Fisher’s, Weighted-Fisher’s

Note: w1 = .14, w2 = .86 for weighted Fisher’s method. Boundaries are shown assuming p2  p1


Additional notation for two other methods basic idea plug in viral load 0 for uninfected subjects

Additional Notation for Two Other MethodsBasic Idea: Plug in viral load = 0 for uninfected subjects


Additional notation for two other methods cont d

Additional Notation for Two Other Methods (cont’d)


Methods for establishing poc cont d2

Methods for Establishing POC (cont’d)


Methods for establishing poc cont d3

Methods for Establishing POC (cont’d)


Illustrative example hypothetical data

Illustrative Example: Hypothetical Data


Illustrative example hypothetical data cont d

Illustrative Example: Hypothetical Data (cont’d)


Simulation study

Simulation Study


Assumed distributions for log10 viral laod

Assumed Distributions for log10(viral laod)

SD = 0.75

Placebo

μ

SD = 0.91

Vaccine

μ - δ

Note: Assumed VL distribution for vaccine is asymmetric and more variable (mixture of vaccine “non-responders” and “responders”)


Simulation study cont d

Simulation Study (cont’d)


Simulation results type i error rate 5

Simulation Results: Type-I Error Rate (=5%)


Simulation results type i error nominal 5

Simulation Results: Type-I Error (nominal =5%)


Simulation results power 5 1 tailed

Simulation Results: Power ( = 5%, 1-tailed)

VE=0%,δ=0.5

VE=0%,δ=1.0


Simulation results power 5 1 tailed1

Simulation Results: Power ( = 5%, 1-tailed)

VE=30%,δ=0.5

VE=30%,δ=1.0


Simulation results power 5 1 tailed2

Simulation Results: Power ( = 5%, 1-tailed)

VE=60%,δ=0.5

VE=60%,δ=1.0


Statistical methodology for evaluating a cell mediated immunity based hiv vaccine

Number of Infections Required for Establishing POC*Simes’, Fisher’s, Weighted-Fisher’smethods80% power, =5% (1-tailed)


Challenge for the merck vaccine

Challenge for the Merck Vaccine

  • Pre-existing immunity to Adenovirus Type 5 may prevent or dampen the T cell response to the HIV proteins

  • In the U.S., ~30-50% of people have neutralizing antibodies to Ad-5 virus

  • In Southern Africa, ~75-95% of people neutralize Ad-5

  • Summary of data from Phase I-II trials

    • Ad-5 Neut Titers < 18: ~80% vaccinees have a CD8+ ELISpot response

    • Ad-5 Neut Titers > 1000: ~40% have a response

    • In responders, geometric mean titer ~200 for vaccinees with Ad-5 Neut Titers < 18; ~100 for vaccinees with Ad-5 Neut Titers > 1000


Concluding remarks

Concluding Remarks

  • For a POC trial of a CMI-based HIV vaccine, Fisher’s (and Simes’) methods are good choices.

  • If the composite null hypothesis is rejected at the 5% level, the p-values for the two endpoints can each be assessed separately at the 5% level.

  • Challenges for the viral load analysis:

    -Initiation of antiretroviral therapy < 3 monthsafter HIV+ diagnosis (“missing” vRNA data)

    -Important to add “sensitivity analyses” to safeguard against potential selection bias (e.g., Gilbert et al, 2003).

    -Estimating causal effect of vaccine on post-infection viral load (ongoing research)


Appendix

Appendix


References

References

  • Chang MN, Guess HA, Heyse JF (1994). Reduction in the burden of illness: a new efficacy measure for prevention trials. Statistics in Medicine, 13, 1807-1814.

  • Chen J, Gould AL, Nessly ML. Comparing two treatments by using a biomarker with assay limit. Statistics in Medicine, in press.

  • Fisher RA (1932). Statistical methods for research workers. Oliver and Boyd, Edinburgh and London.

  • Follman D (1995). Multivariate tests for multiple endpoints in clinical trials. Statistics in Medicine, 14, 1163-1175.

  • Gilbert PB, Bosch RJ, Hudgens MG. Sensitivity analysis for the assessment of causal vaccine effects on viral load in HIv vaccine clinical trials. Biometrics, 59, 531-541.

  • Good IJ (1955). On the weighted combination of significance tests. Biometrika, 264-265.

  • Hochberg Y, Liberman U (1994). An extended Simes’ test. Statistics & Probability Letters, 21, 101-105.

  • Lachenbruch PA (1976). Analysis of data with clumping at zero. Biometrische Zeitschrift, 18, 351-356.

  • O’Brien PC (1984). Procedures for comparing samples with multiple endpoints. Biometrics, 40, 1079-1087.


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