Statistical Methodology for Evaluating a Cell Mediated Immunity-Based HIV Vaccine Devan V. Mehrotra* and Xiaoming Li Merck Research Laboratories, Blue Bell, PA * Biostat 578A Lecture 4 Adapted from Devan’s presentation at the ASA/Northeastern Illinois Chapter Meeting October 14, 2004

2 Outline Science behind the numbers Merck’s HIV vaccine project Proof of concept (POC) efficacy study Statistical methods Simulation study Concluding remarks

3 Worldwide Distribution of HIV-1 Clades (Subtypes)* Note:*Dominant clades are bolded above; All regions have multiple clades in their populations B B, BC B C A, B, AB, Other G B, F, Other B, F B, AE B, Other AE, B, Other B, AE, Other B, Other B O B, O A A All C C, Other B, Other A, Other G, Other AG A,C,D Legend B dominant + Another C O A All B, AE B, G, Other C B, C F Other B

4 T Cell Recognition of Infected Cells

5 HIV Infection: CD4 cell count and Viral Load

6 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.

7 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

8 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

9 Notation for Statistical Methodology

10 Notation (cont’d)

11 Notation (cont’d)

12 Competing Methods for Establishing POC

13 Optimal Weights for Viral Load Component of Composite Test (w 2 ) under Different Scenarios True VE (%) δ = true mean diff. (placebo – vaccine) in log 10 (vRNA) among infected subjects %~1 15% % % % % %

14 Methods for Establishing POC (cont’d)

15 Methods for Establishing POC (cont’d)

16 Illustration of Simes, Weighted-Simes, Fisher’s, Weighted-Fisher’s Methods (Hypothetical Examples) Note: w 1 =.14, w 2 =.86 for weighted-Simes’ and weighted-Fisher’s methods

17 Critical Boundaries: Simes, Weighted-Simes, Fisher’s, Weighted-Fisher’s Note: w 1 =.14, w 2 =.86 for weighted Fisher’s method. Boundaries are shown assuming p2  p1

18 Additional Notation for Two Other Methods Basic Idea: Plug in viral load = 0 for uninfected subjects

19 Additional Notation for Two Other Methods (cont’d)

20 Methods for Establishing POC (cont’d)

21 Methods for Establishing POC (cont’d)

22 Illustrative Example: Hypothetical Data

23 Illustrative Example: Hypothetical Data (cont’d)

24 Simulation Study

25 Assumed Distributions for log10(viral laod) Placebo μ - δ SD = 0.75 SD = 0.91 Vaccine μ Note: Assumed VL distribution for vaccine is asymmetric and more variable (mixture of vaccine “non-responders” and “responders”)

26 Simulation Study (cont’d)

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

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

29 Simulation Results: Power (  = 5%, 1-tailed) VE=0%, δ=0.5VE=0%, δ=1.0

30 Simulation Results: Power (  = 5%, 1-tailed) VE=30%, δ=0.5VE=30%, δ=1.0

31 Simulation Results: Power (  = 5%, 1-tailed) VE=60%, δ=0.5VE=60%, δ=1.0

32 Number of Infections Required for Establishing POC* Simes’, Fisher’s, Weighted-Fisher’s methods 80% power,  =5% (1-tailed)

33 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 1000

34 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 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)

35 Appendix

36 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, 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, 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, Good IJ (1955). On the weighted combination of significance tests. Biometrika, Hochberg Y, Liberman U (1994). An extended Simes’ test. Statistics & Probability Letters, 21, Lachenbruch PA (1976). Analysis of data with clumping at zero. Biometrische Zeitschrift, 18, O’Brien PC (1984). Procedures for comparing samples with multiple endpoints. Biometrics, 40,