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

2025-12-09

AI Summary

Traditional vaccine efficacy analyses—relying on single-sequence assumptions—are inadequate for highly genetically diverse pathogens like HIV, failing to capture the complex intra-host viral variant spectra revealed by deep sequencing. To address this, we propose a novel statistical framework that integrates a competing-risks Cox model with latent-variable modeling of failure types and introduces an empirical Bayes–based misclassification correction method. This enables robust estimation of differential vaccine efficacy against viral populations harboring varying proportions of immune-escape mutations. In simulations, our approach substantially reduces estimation bias, maintains nominal confidence interval coverage, and improves statistical power. We applied it to the HVTN 705 (Imbokodo) HIV vaccine trial, demonstrating its practical utility. The framework provides a generalizable, statistically rigorous tool for evaluating vaccines against highly diverse pathogens.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Large-scale security measurements

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.

Problem

• Extends sieve analysis to incorporate intra-individual viral diversity in vaccine studies.
• Estimates vaccine efficacy against viral populations with varying mutation frequencies.
• Addresses differential sequence counts using competing risks Cox regression with misclassification.

Innovation

• Competing risks Cox regression with misclassification modeling
• Empirical Bayes approach for failure type classification
• Extended sieve analysis for intra-individual viral diversity