ArticleThe annals of applied statistics2020
CAUSAL INFERENCE FROM OBSERVATIONAL STUDIES WITH CLUSTERED INTERFERENCE, WITH APPLICATION TO A CHOLERA VACCINE STUDY.
Article in The annals of applied statistics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Joint mixed-effects models for causal inference in clustered network-based observational studies.Statistical methods in medical research · 2026Article
- A causal framework for evaluating drivers of policy effect heterogeneity using difference-in-differences.Health services & outcomes research methodology · 2026Article
- G-estimation with partial interference.Stat (International Statistical Institute) · 2025Article
- Design of field trials for the evaluation of transmissible vaccines in animal populations.PLoS computational biology · 2025Article
- Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference.Journal of the American Statistical Association · 2025Article
- G-formula for observational studies under stratified interference, with application to bed net use on malaria.Statistics in medicine · 2024Article
- Article
- Methods for Assessing Spillover in Network-Based Studies of HIV/AIDS Prevention among People Who Use Drugs.Pathogens (Basel, Switzerland) · 2023Article
- Propensity Score in the Face of Interference: Discussion of.Observational studies · 2023Article
- Inverse probability weighted estimators of vaccine effects accommodating partial interference and censoring.Biometrics · 2022Article
- A Review of Spatial Causal Inference Methods for Environmental and Epidemiological Applications.International statistical review = Revue internationale de statistique · 2021Article
- Assortativity and Bias in Epidemiologic Studies of Contagious Outcomes: A Simulated Example in the Context of Vaccination.American journal of epidemiology · 2021Article
- Evaluation of the health impacts of the 1990 Clean Air Act Amendments using causal inference and machine learning.Journal of the American Statistical Association · 2020Article
- CAUSAL INFERENCE FROM OBSERVATIONAL STUDIES WITH CLUSTERED INTERFERENCE, WITH APPLICATION TO A CHOLERA VACCINE STUDY.The annals of applied statistics · 2020Article
- Estimands and inference in cluster-randomized vaccine trials.Pharmaceutical statistics · 2020Article
- A causal inference framework for cancer cluster investigations using publicly available data.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2020Article
- Causal inference with interfering units for cluster and population level treatment allocation programs.Biometrics · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Understanding the population-level effects of vaccines has important public health policy implications. Inferring vaccine effects from an observational study is challenging because participants are not randomized to vaccine (i.e., treatment). Observational studies of infectious diseases present the additional challenge that vaccinating one participant may affect another participant's outcome, i.e., there may be interference. In this paper recent approaches to defining vaccine effects in the presence of interference are considered, and new causal estimands designed specifically for use with observational studies are proposed. Previously defined estimands target counterfactual scenarios in which individuals independently choose to be vaccinated with equal probability. However, in settings where there is interference between individuals within clusters, it may be unlikely that treatment selection is independent between individuals in the same cluster. The proposed causal estimands instead describe counterfactual scenarios which allow for within-cluster dependence in the individual treatment selections. These estimands may be more relevant for policymakers or public health officials who desire to quantify the effect of increasing the proportion of vaccinated individuals in a population. Inverse probability-weighted estimators for these estimands are proposed. The large-sample properties of the estimators are derived, and a simulation study demonstrating the finite-sample performance of the estimators is presented. The proposed methods are illustrated by analyzing data from a study of cholera vaccination in over 100,000 individuals in Bangladesh.
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