Assessing surrogate endpoints in vaccine trials with case-cohort sampling and the Cox model

TL;DR

This paper develops methods to evaluate immune responses as surrogate endpoints in vaccine trials using case-cohort sampling and Cox models, addressing missing data and unmeasured responses.

Contribution

It introduces novel estimands and trial designs for surrogate endpoint assessment under the principal stratification framework with missing immune response data.

Findings

Proposed estimands measure immune response as a surrogate in Cox models.

Two innovative trial designs predict missing immune responses for surrogate validation.

Simulation studies demonstrate the effectiveness of the proposed methods.

Abstract

Assessing immune responses to study vaccines as surrogates of protection plays a central role in vaccine clinical trials. Motivated by three ongoing or pending HIV vaccine efficacy trials, we consider such surrogate endpoint assessment in a randomized placebo-controlled trial with case-cohort sampling of immune responses and a time to event endpoint. Based on the principal surrogate definition under the principal stratification framework proposed by Frangakis and Rubin [Biometrics 58 (2002) 21--29] and adapted by Gilbert and Hudgens (2006), we introduce estimands that measure the value of an immune response as a surrogate of protection in the context of the Cox proportional hazards model. The estimands are not identified because the immune response to vaccine is not measured in placebo recipients. We formulate the problem as a Cox model with missing covariates, and employ novel trial designs for predicting the missing immune responses and thereby identifying the estimands. The first design utilizes information from baseline predictors of the immune response, and bridges their relationship in the vaccine recipients to the placebo recipients. The second design provides a validation set for the unmeasured immune responses of uninfected placebo recipients by immunizing them with the study vaccine after trial closeout. A maximum estimated likelihood approach is proposed for estimation of the parameters. Simulated data examples are given to evaluate the proposed designs and study their properties.