Causal identification of infectious disease intervention effects in a clustered population

by   Xiaoxuan Cai, et al.

Causal identification of treatment effects for infectious disease outcomes in interconnected populations is challenging because infection outcomes may be transmissible to others, and treatment given to one individual may affect others' outcomes. Contagion, or transmissibility of outcomes, complicates standard conceptions of treatment interference in which an intervention delivered to one individual can affect outcomes of others. Several statistical frameworks have been proposed to measure causal treatment effects in this setting, including structural transmission models, mediation-based partnership models, and randomized trial designs. However, existing estimands for infectious disease intervention effects are of limited conceptual usefulness: Some are parameters in a structural model whose causal interpretation is unclear, others are causal effects defined only in a restricted two-person setting, and still others are nonparametric estimands that arise naturally in the context of a randomized trial but may not measure any biologically meaningful effect. In this paper, we describe a unifying formalism for defining nonparametric structural causal estimands and an identification strategy for learning about infectious disease intervention effects in clusters of interacting individuals when infection times are observed. The estimands generalize existing quantities and provide a framework for causal identification in randomized and observational studies, including situations where only binary infection outcomes are observed. A semiparametric class of pairwise Cox-type transmission hazard models is used to facilitate statistical inference in finite samples. A comprehensive simulation study compares existing and proposed estimands under a variety of randomized and observational vaccine trial designs.



There are no comments yet.


page 1

page 2

page 3

page 4


Identification of causal intervention effects under contagion

Defining and identifying causal intervention effects for transmissible i...

Randomization for the direct effect of an infectious disease intervention in a clustered study population

Randomized trials of infectious disease interventions often focus on pop...

Causal Inference from Observational Studies with Clustered Interference

Inferring causal effects from an observational study is challenging beca...

Statistical Analysis Plan for Health Outcomes in Phase 1 of the SEARCH-IPT Study

This document provides the statistical analytic plan (SAP) for evaluatin...

Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data

Estimating personalized treatment effects from high-dimensional observat...

Identification of vaccine effects when exposure status is unknown

Results from randomized controlled trials (RCTs) help determine vaccinat...

Evaluating the impact of local tracing partnerships on the performance of contact tracing for COVID-19 in England

Assessing the impact of an intervention using time-series observational ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.