Identification of causal intervention effects under contagion.

Forrest W. Crawford,Wen Wei Loh,Xiaoxuan Cai

doi:10.1515/jci-2019-0033

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.

Highlights

Estimating the causal effect of an intervention can be challenging when the outcome of interest is contagious [41]
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
These new estimands are compared to existing quantities, and results are illustrated using a realistic simulation of an HIV vaccine trial

Summary

Introduction

Estimating the causal effect of an intervention can be challenging when the outcome of interest is contagious [41]. Researchers have described two-person partnership models of infectious disease transmission for defining more granular, or individual, causal intervention effects. When the outcomes are time-dependent processes – as is often in infectious disease transmission dynamics – binary outcome indicators and specified time windows may be used to define outcomes so that the mediationbased approaches may be applied These definitions can complicate identification of causal effects because (i) a repeatedly measured outcome over time may introduce multiple mediators, and (ii) absence of the outcome at prior time points as a prerequisite for later measurements induces time-varying confounding. We seek to combine approaches from causal mediation analysis and mathematical modeling of transmission to develop a nonparametric framework that formalizes the role of time in infectious disease transmission from a causal perspective. We discuss nonparametric identification under randomization and in observational settings, compare these estimands to existing quantities proposed by other authors, and conduct a simulation analysis of a hypothetical HIV vaccine trial to illustrate the estimands

Setting

Assumptions

Causal estimands

Identification of potential infection outcomes

Comparison to other infectious disease intervention effects

Application: a hypothetical vaccine trial

Discussion

A Proofs

Statistical estimation for the controlled potential outcomes in Theorem 1

Statistical estimation for the natural potential outcomes in Corollary 1