Vaccine sieve analysis on deep sequencing data using competing risks Cox regression with failure type subject to misclassification

TL;DR

This paper develops a novel statistical method extending vaccine sieve analysis to account for intra-individual viral diversity using competing risks Cox regression and empirical Bayes, improving bias reduction and power.

Contribution

It introduces a new methodology that incorporates intra-individual viral diversity into sieve analysis, enhancing accuracy and power in vaccine efficacy studies.

Findings

Method reduces bias in efficacy estimates.

Simulation shows improved confidence interval coverage.

Application to HIV trial demonstrates practical utility.

Abstract

Understanding how vaccines perform against different pathogen genotypes is crucial for developing effective prevention strategies, particularly for highly genetically diverse pathogens like HIV. Sieve analysis is a statistical framework used to determine whether a vaccine selectively prevents acquisition of certain genotypes while allowing breakthrough of other genotypes that evade immune responses. Traditionally, these analyses are conducted with a single sequence available per individual acquiring the pathogen. However, modern sequencing technology can provide detailed characterization of intra-individual viral diversity by capturing up to hundreds of pathogen sequences per person. In this work, we introduce methodology that extends sieve analysis to account for intra-individual viral diversity. Our approach estimates vaccine efficacy against viral populations with varying true (unobservable) frequencies of vaccine-mismatched mutations. To account for differential resolution of information from differing sequence counts per person, we use competing risks Cox regression with modeled causes of failure and propose an empirical Bayes approach for the classification model. Simulation studies demonstrate that our approach reduces bias, provides nominal confidence interval coverage, and improves statistical power compared to conventional methods. We apply our method to the HVTN 705 Imbokodo trial, which assessed the efficacy of a heterologous vaccine regimen in preventing HIV-1 acquisition.