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

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 Immunity-Based HIV Vaccine

  • 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)* Immunity-Based HIV Vaccine

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 Immunity-Based HIV Vaccine



Merck s hiv vaccine project
Merck’s HIV Vaccine Project Immunity-Based HIV Vaccine

  • 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 Immunity-Based HIV Vaccine

  • 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) Immunity-Based HIV Vaccine

  • 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 Immunity-Based HIV Vaccine


Notation cont d
Notation (cont’d) Immunity-Based HIV Vaccine


Notation cont d1
Notation (cont’d) Immunity-Based HIV Vaccine


Competing methods for establishing poc
Competing Methods for Establishing POC Immunity-Based HIV Vaccine


Optimal weights for viral load component of composite test w 2 under different scenarios
Optimal Weights for Immunity-Based HIV VaccineViral Load Component of Composite Test (w2) under Different Scenarios


Methods for establishing poc cont d
Methods for Establishing POC (cont’d) Immunity-Based HIV Vaccine


Methods for establishing poc cont d1
Methods for Establishing POC (cont’d) Immunity-Based HIV Vaccine


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 Weighted-Fisher’s Methods (Hypothetical Examples): 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 Methods Weighted-Fisher’s Methods (Hypothetical Examples)Basic 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) Weighted-Fisher’s Methods (Hypothetical Examples)


Methods for establishing poc cont d2
Methods for Establishing POC (cont’d) Weighted-Fisher’s Methods (Hypothetical Examples)


Methods for establishing poc cont d3
Methods for Establishing POC (cont’d) Weighted-Fisher’s Methods (Hypothetical Examples)


Illustrative example hypothetical data
Illustrative Example: Hypothetical Data Weighted-Fisher’s Methods (Hypothetical Examples)


Illustrative example hypothetical data cont d
Illustrative Example: Hypothetical Data (cont’d) Weighted-Fisher’s Methods (Hypothetical Examples)


Simulation study
Simulation Study Weighted-Fisher’s Methods (Hypothetical Examples)


Assumed distributions for log10 viral laod
Assumed Distributions for log10(viral laod) Weighted-Fisher’s Methods (Hypothetical Examples)

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) Weighted-Fisher’s Methods (Hypothetical Examples)


Simulation results type i error rate 5
Simulation Results: Type-I Error Rate ( Weighted-Fisher’s Methods (Hypothetical Examples)=5%)


Simulation results type i error nominal 5
Simulation Results: Type-I Error (nominal Weighted-Fisher’s Methods (Hypothetical Examples)=5%)


Simulation results power 5 1 tailed
Simulation Results: Weighted-Fisher’s Methods (Hypothetical Examples)Power ( = 5%, 1-tailed)

VE=0%,δ=0.5

VE=0%,δ=1.0


Simulation results power 5 1 tailed1
Simulation Results: Weighted-Fisher’s Methods (Hypothetical Examples)Power ( = 5%, 1-tailed)

VE=30%,δ=0.5

VE=30%,δ=1.0


Simulation results power 5 1 tailed2
Simulation Results: Weighted-Fisher’s Methods (Hypothetical Examples)Power ( = 5%, 1-tailed)

VE=60%,δ=0.5

VE=60%,δ=1.0


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


Challenge for the merck vaccine
Challenge for the Merck Vaccine Weighted-Fisher’s Methods (Hypothetical Examples)

  • 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 Weighted-Fisher’s Methods (Hypothetical Examples)

  • 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 months after 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 Weighted-Fisher’s Methods (Hypothetical Examples)


References
References Weighted-Fisher’s Methods (Hypothetical Examples)

  • 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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