ReviewSpatial and spatio-temporal epidemiology2020
NIMBLE for Bayesian Disease Mapping.
Review in Spatial and spatio-temporal epidemiology, 2020. 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 4 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
4 citing papers in PubMed.
- Large-scale importance of bark beetle outbreaks for standing deadwood and woodpeckers.The Journal of animal ecology · 2025Review
- A Bayesian modelling framework to quantify multiple sources of spatial variation for disease mapping.Journal of the Royal Society, Interface · 2022Article
- A joint hierarchical model for the number of cases and deaths due to COVID-19 across the boroughs of Montreal.Spatial and spatio-temporal epidemiology · 2022Article
- A MULTIVARIATE SPATIOTEMPORAL CHANGE-POINT MODEL OF OPIOID OVERDOSE DEATHS IN OHIO.The annals of applied statistics · 2021Article
Corrections and comments
- Erratum issued
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This tutorial describes the basic implementation of Bayesian hierarchical models for spatial health data using the R package nimble. To quote the nimble R description: A system for writing hierarchical statistical models largely compatible with 'BUGS' and 'JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. 'NIMBLE' includes default methods for MCMC, particle filtering, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers 'NIMBLE' provides. Examples of the use of the package for a small range of Bayesian Disease Mapping (BDM) models is explored and focus on different approaches to model fitting and analysis are discussed. Examples of publicly available small area health data is used throughout.
Indexed as
Identifiers
32370936What OpenQuestion holds
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.