ArticleEpidemiology (Cambridge, Mass.)2012
A mapping between interactions and interference: implications for vaccine trials.
Article in Epidemiology (Cambridge, Mass.), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Neighborhood effects in a behavioral randomized controlled trial.Health & place · 2014Trial
- A proof of Bell's inequality in quantum mechanics using causal interactions.Scandinavian journal of statistics, theory and applications · 2015Article
- Interference and Sensitivity Analysis.Statistical science : a review journal of the Institute of Mathematical Statistics · 2014Article
- Causal Inference for a Population of Causally Connected Units.Journal of causal inference · 2014Article
- Large sample randomization inference of causal effects in the presence of interference.Journal of the American Statistical Association · 2014Article
- Estimation of vaccine efficacy and critical vaccination coverage in partially observed outbreaks.PLoS computational biology · 2013Article
- Extending the sufficient component cause model to describe the Stable Unit Treatment Value Assumption (SUTVA).Epidemiologic perspectives & innovations : EP+I · 2012Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
In this paper, we discuss relationships between causal interactions within the counterfactual framework and interference in which the exposure of one person may affect the outcomes of another. We show that the empirical tests for causal interactions can, in fact, all be adapted to empirical tests for particular forms of interference. In the context of interference, by recoding the response as some function of the outcomes of the various persons within a cluster, a wide range of different forms of interference can potentially be detected. The correspondence between causal interactions and forms of interference extends to encompass n-way causal interactions, interference between n persons within a cluster, and multivalued exposures. The theory for causal interactions provides a complete conceptual apparatus for assessing interference as well. The results are illustrated using data from a hypothetical vaccine trial to reason about specific forms of interference and spillover effects that may be present in this vaccine setting. We discuss the implications of this correspondence for our conceptualizations of interaction and for application to vaccine trials and many other settings in which spillover effects may be present.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.