# Statistical Methods for Efficacy Trials of Vaccines and Monoclonal Antibodies Against Genetically-Diverse Pathogens

> **NIH NIH R37** · FRED HUTCHINSON CANCER CENTER · 2022 · $426,899

## Abstract

PROJECT SUMMARY/ABSTRACT
Safe and globally efficacious vaccines are needed to massively reduce the economic and human health costs
of HIV-1/AIDS, dengue, and malaria. This goal has been hindered to varying extents by the genetic diversity of
HIV-1, dengue virus, and Plasmodium falciparum, with the best available vaccines having only low-to-moderate
or variable efficacy against these pathogens. Randomized, controlled clinical trials that rigorously assess the
efficacy of candidate vaccines to prevent infection and/or disease caused by such pathogens are a core research
platform for developing improved vaccines. In addition, randomized clinical efficacy trials of broadly neutralizing
monoclonal antibody (bnAb) regimens aid vaccine development. This project develops statistical methods for
the design and analysis of vaccine and bnAb prevention efficacy trials, with purpose to rigorously characterize
multiple types of distinct and complementary “immune correlates,” which are critical tools for driving the iterative
research process for improving vaccines. Aim 1 develops methods for assessing immunological markers
measured over time as correlates of instantaneous risk of acquisition of HIV-1 (of any strain or with a strain with
a particular feature such as an amino acid (AA) sequence or serotype) in (a) HIV-1 vaccine and (b) HIV-1 bnAb
efficacy trials; such correlates are especially helpful for generating hypotheses and insights about mechanisms
of protection. Aim 2 develops methods for assessing immune response markers measured by a given fixed time
point post-vaccination in HIV-1, dengue, and malaria vaccine efficacy (VE) trials as two types of correlates of
protection: (a) an estimated optimal surrogate, which is an optimal summary marker combining information from
all markers that best predicts overall and feature-specific infection or disease occurrence over a specified
cumulative period of time; and (b) a correlate of VE, which is a summary marker that is a modifier/predictor of
the level of VE. Aim 3 develops dynamic recurrent event models for assessing (a) malaria VE against overall
and circumsporozoite protein (CSP) AA-specific malaria infection and disease, and (b) how CSP AA-specific
malaria risk depends on prior immune responses and malaria infections, improving models of vaccine- and
natural-immunity. Aim 4, in recognizing the importance of pre-exposure prophylaxis (PrEP) as an effective
modality for reducing HIV-1 acquisition, develops causal methods for assessing vaccine and bnAb efficacy to
prevent (a) overall and (b) feature-specific HIV-1 infection in study populations defined by certain patterns of
PrEP use, including zero use. The methods will be developed with application to 8 recently completed or ongoing
VE trials (4 for HIV-1, 2 for dengue, 2 for malaria) and 2 bnAb efficacy trials for HIV-1. The two ongoing bnAb
trials and the ongoing malaria VE trial are particularly groundbreaking; the former is the first evaluation of a bnA...

## Key facts

- **NIH application ID:** 10441303
- **Project number:** 5R37AI054165-21
- **Recipient organization:** FRED HUTCHINSON CANCER CENTER
- **Principal Investigator:** Peter B. Gilbert
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $426,899
- **Award type:** 5
- **Project period:** 2003-04-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10441303

## Citation

> US National Institutes of Health, RePORTER application 10441303, Statistical Methods for Efficacy Trials of Vaccines and Monoclonal Antibodies Against Genetically-Diverse Pathogens (5R37AI054165-21). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10441303. Licensed CC0.

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