Proportional hazards models with continuous marks

TL;DR

This paper extends proportional hazards models to handle continuous marks observed at failure times, enabling analysis of complex competing risks like genetic divergence in HIV vaccine trials.

Contribution

It introduces a nonparametric inference framework for proportional hazards models with continuous marks, applicable to real-world scenarios such as HIV vaccine efficacy assessment.

Findings

Developed a new inference method for continuous mark proportional hazards models.

Applied the model to HIV vaccine trial data, demonstrating its practical utility.

Validated the approach through simulation studies.

Abstract

For time-to-event data with finitely many competing risks, the proportional hazards model has been a popular tool for relating the cause-specific outcomes to covariates [Prentice et al. Biometrics 34 (1978) 541--554]. This article studies an extension of this approach to allow a continuum of competing risks, in which the cause of failure is replaced by a continuous mark only observed at the failure time. We develop inference for the proportional hazards model in which the regression parameters depend nonparametrically on the mark and the baseline hazard depends nonparametrically on both time and mark. This work is motivated by the need to assess HIV vaccine efficacy, while taking into account the genetic divergence of infecting HIV viruses in trial participants from the HIV strain that is contained in the vaccine, and adjusting for covariate effects. Mark-specific vaccine efficacy is expressed in terms of one of the regression functions in the mark-specific proportional hazards model. The new approach is evaluated in simulations and applied to the first HIV vaccine efficacy trial.