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
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
A, B, AB, Other
B, G, Other
AE, B, Other
B, AE, Other
B, F, Other
B dominant + Another
Note:*Dominant clades are bolded above; All regions have multiple clades in their populations
HIV Infection: CD4 cell count and Viral Load
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.
-Randomized, double-blind, placebo-controlled
- Subjects at high risk of acquiring HIV infection
- HIV diagnostic test every 6 mos. (~ 3 yrs. f/up)
-HIV infection status (infected/uninfected)
-Viral load set-point (vRNA at ~ 3 months after diagnosis of HIV infection)
Same HIV infection rates (VE = 0) and
Same distribution of viral load among infected subjs.
Lower HIV infection rate (VE > 0) and/or
Lower viral load for infected subjects who got vaccine
Note: w1 = .14, w2 = .86 for weighted-Simes’ and weighted-Fisher’s methods
Note: w1 = .14, w2 = .86 for weighted Fisher’s method. Boundaries are shown assuming p2 p1
SD = 0.75
SD = 0.91
μ - δ
Note: Assumed VL distribution for vaccine is asymmetric and more variable (mixture of vaccine “non-responders” and “responders”)
-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)