A controlled effects approach to assessing immune correlates of protection

Abstract

An immune correlate of risk (CoR) is an immunologic biomarker in vaccine recipients associated with an infectious disease clinical endpoint. An immune correlate of protection (CoP) is a CoR that can be used to reliably predict vaccine efficacy (VE) against the clinical endpoint and hence is accepted as a surrogate endpoint that can be used for accelerated approval or guide use of vaccines. In randomized, placebo-controlled trials, CoR analysis is limited by not assessing a causal vaccine effect. To address this limitation, we construct the controlled risk curve of a biomarker, which provides the causal risk of an endpoint if all participants are assigned vaccine and the biomarker is set to different levels. Furthermore, we propose a causal CoP analysis based on controlled effects, where for the important special case that the biomarker is constant in the placebo arm, we study the controlled vaccine efficacy curve that contrasts the controlled risk curve with placebo arm risk. We provide identification conditions and formulae that account for right censoring of the clinical endpoint and two-phase sampling of the biomarker, and consider G-computation estimation and inference under a semiparametric model such as the Cox model. We add modular approaches to sensitivity analysis that quantify robustness of CoP evidence to unmeasured confounding. We provide an application to two phase 3 trials of a dengue vaccine indicating that controlled risk of dengue strongly varies with 50$\%$ neutralizing antibody titer. Our work introduces controlled effects causal mediation analysis to immune CoP evaluation.

Keywords: COVID-19 vaccine; Controlled direct effects; Dengue vaccine efficacy; E-value; Immune correlate of protection; Sensitivity analysis.

Fig. 1.

(A) DAG expressing the causal assumptions (A2.1) (initial randomization) and (A2.2) (strong sequential ignorability); (B) DAG relaxing (A2.2) to a more realistic causal model that allows an unmeasured confounder of the effect of on . The variables in light gray are unobserved.

Fig. 2.

and surfaces for with user-supplied sensitivity parameter with the median of and the 95th percentile of (the specified degree of unmeasured confounding) where

Fig. 3.

Analysis of M13 average titer (quantitative with anti-log x-axis label) as a CoR and a controlled risk CoP: CYD14 and CYD15 dengue VE trials. Solid lines are point estimates and dashed lines are 95 CIs. The bands that start higher on the left with a steeper shape are for marginalized risk and the bands with a shallower shape are conservative estimates of controlled risk , with and hence , where and are 15th and 85th percentiles of .

Fig. 4.

Analysis of M13 average titer (quantitative with anti-logx-axis label) as a CVE CoP: CYD14 and CYD15 dengue VE trials. Solid lines are point estimates of for with fixed at the median of the distribution of [ for CYD14 (CYD15)] and dashed lines are 95 CIs. The faint lines are estimates of assuming no unmeasured confounding and the darker lines are conservative estimates of accounting for potential unmeasured confounding, using the same sensitivity parameters as for Figure 3.