ArticleStatistics in medicine2020
Modeling peer effect modification by network strength: The diffusion of implantable cardioverter defibrillators in the US hospital network.
Article in Statistics in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 19 citations in OpenAlex.
- Evaluating the role of visit audio recordings in triadic dementia care: study protocol.BMC geriatrics · 2025Article
- A Scoping Review of Multilevel Patient-Sharing Network Measures in Health Services Research.Medical care research and review : MCRR · 2025Article
- Developing and Comparing Four Families of Bayesian Network Autocorrelation Models for Binary Outcomes: Estimating Peer Effects Involving Adoption of Medical Technologies.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Influence of multiple network structures on bayesian estimation of peer effects and statistical power for generalized linear network autocorrelation models.Applied network science · 2025Article
- Exploiting relationship directionality to enhance statistical modeling of peer-influence across social networks.Statistics in medicine · 2024Article
- Bayesian Hierarchical Network Autocorrelation Models for Estimating Direct and Indirect Effects of Peer Hospitals on Outcomes of Hospitalized Patients.Research square · 2024Article
- Bayesian hierarchical network autocorrelation models for estimating direct and indirect effects of peer hospitals on outcomes of hospitalized patients.Applied network science · 2024Article
- Constructing within and between hospital physician social networks for modeling physician research participation.BMC medical research methodology · 2023Article
- The diffusion of health care fraud: A bipartite network analysis.Social science & medicine (1982) · 2023Article
- The association of insurance plan characteristics with physician patient-sharing network structure.International journal of health economics and management · 2021Article
- The impact of sampling patients on measuring physician patient-sharing networks using Medicare data.Health services research · 2020Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
We develop methodology that allows peer effects (also referred to as social influence and contagion) to be modified by the structural importance of the focal actor's position in the network. The methodology is first developed for a single peer effect and then extended to simultaneously model multiple peer-effects and their modifications by the structural importance of the focal actor. This work is motivated by the diffusion of implantable cardioverter defibrillators (ICDs) in patients with congestive heart failure across a cardiovascular disease patient-sharing network of United States hospitals. We apply the general methodology to estimate peer effects for the adoption of capability to implant ICDs, the number of ICD implants performed by hospitals that are capable, and the number of patients referred to other hospitals by noncapable hospitals. Applying our novel methodology to study ICD diffusion across hospitals, we find evidence that exposure to ICD-capable peer hospitals is strongly associated with the chance a hospital becomes ICD-capable and that the direction and magnitude of the association is extensively modified by the strength of that hospital's position in the network, even after controlling for effects of geography. Therefore, interhospital networks, rather than geography per se, may explain key patterns of regional variations in healthcare utilization.
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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.