Year: 2021
Type: article
Abstract: Defining and identifying causal intervention effects for transmissible infectious disease outcomes is challenging because a treatment - such as a vaccine - given to one individual may affect the infection outcomes of others. Epidemiologists have proposed causal estimands to quantify effects of interventions under contagion using a two-person partnership model. These simple conceptual models have helped researchers develop causal estimands relevant to clinical evaluation of vaccine effects. However, many of these partnership models are formulated under structural assumptions that preclude realistic infectious disease transmission dynamics, limiting their conceptual usefulness in defining and identifying causal treatment effects in empirical intervention trials. In this paper, we propose causal intervention effects in two-person partnerships under arbitrary infectious disease transmission dynamics, and give nonparametric identification results showing how effects can be estimated in empirical trials using time-to-infection or binary outcome data. The key insight is that contagion is a causal phenomenon that induces conditional independencies on infection outcomes that can be exploited for the identification of clinically meaningful causal estimands. These new estimands are compared to existing quantities, and results are illustrated using a realistic simulation of an HIV vaccine trial.
Source: Journal of Causal Inference
Authors: Xiaoxuan Cai, Wen Wei Loh, Forrest W. Crawford
Institutions: Yale University, Ghent University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,187
Awards: G1726395408, Causal Inference in Infectious Disease Prevention Studies, Evaluating health and economic effects of targeted strategies in TB/HIV, Network-based epidemiology for hidden and hard-to-reach populations, Modeling the impact and cost-effectiveness of next-generation rotavirus vaccine strategies in low- and middle-income countries
OpenAlex ID: W3145859224