An information-theoretic approach for the assessment of a continuous outcome as a surrogate for a binary true endpoint based on causal inference: Application to vaccine evaluation.

Within the causal association paradigm, a method is proposed to assess the validity of a continuous outcome as a surrogate for a binary true endpoint. The methodology is based on a previously introduced information-theoretic definition of surrogacy and has two main steps. In the first step, a new model is proposed to describe the joint distribution of the potential outcomes associated with the putative surrogate and the true endpoint of interest. The identifiability issues inherent to this type of models are handled via sensitivity analysis. In the second step, a metric of surrogacy new to this setting, the so-called individual causal association is presented. The methodology is studied in detail using theoretical considerations, some simulations, and data from a randomized clinical trial evaluating an inactivated quadrivalent influenza vaccine. A user-friendly R package Surrogate is provided to carry out the evaluation exercise.

Comparing COVID-19 incidences longitudinally per economic sector against the background of preventive measures and vaccination.

In the COVID-19 pandemic, workplace transmission plays an important role. For this type of transmission, the longitudinal 14-day incidence curve of SARS-CoV-2 infections per economic sector is a proxy. In Belgium, a census of confirmed 14-day incidences per NACE-BEL sector level three is available from September 2020 until June 2021, encompassing two waves of infections. However, these high-dimensional data, with a relatively small number of NACE-BEL sectors, are challenging to analyze. We propose a nonlinear Gaussian-Gaussian model that combines parametric and semi-parametric elements to describe the incidence curves with a small set of meaningful parameters. These parameters are further analyzed with conventional statistical methods, such as CCA and linear models, to provide insight into predictive characteristics of the first wave for the second wave. Those nonlinear models classify economic sectors into three groups: sectors with two regular waves of infections, sectors with only a first wave and sectors with a more irregular profile, which may indicate a clear effect of COVID-19 vaccination. The Gaussian-Gaussian model thus allows for analyzing and comparing incidence curves and to bring out key characteristics of such curves. Finally, we consider in which other settings the proposed approach could be applied, together with possible pitfalls.