Disease Mapping

Waller, Lance A.; Carlin, Bradley P.

doi:10.1201/9781420072884-c14

Cited by 62 publications

(47 citation statements)

References 77 publications

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“…As mentioned by Waller and Carlin [84] DM refers to a collection of methods extending SAE to directly utilize the spatial setting and assumed positive spatial correlation between observations. The data used are aggregated or averaged values at the small area level, representing disease incidence, prevalence or mortality rates, frequently not coming from surveys but coming from counts of disease cases from hospital admissions [52,59]), counts of cancer cases or cancer deaths [9,47,82]), and mortality data [20,59,60]).…”

Section: Dm As a Special Case Of Saementioning

confidence: 99%

“…Other adjacency-based weights are available but are much less widely applied [84]). If two areas are neighbors their random effects are correlated, while non-neighboring areas are modeled as being conditionally independent given the remaining elements of φ.…”

Section: Disease Mapping Modelsmentioning

confidence: 99%

“…DM methodologies are explained in Waller and Carlin [84] and Banerjee et al [5]. DM methodologies for areal data are usually divided in frequentist methods and hierarchical Bayesian models [5].…”

Section: Disease Mapping Modelsmentioning

confidence: 99%

“…This concept has become so important that we can find the following definition of DM: [84] (our underlining) "In the statistical literature, "disease mapping" refers to a collection of methods extending small area estimation to directly utilize the spatial setting and assumed positive spatial correlation between observations, essentially borrowing more information from neighboring areas than from areas far away and smoothing local rates toward local, neighboring values. "…”

Section: Introductionmentioning

confidence: 99%

“…It uses the spatial setting and assumes positive spatial correlation between observations, essentially "borrowing" more information from neighboring areas than from areas far away, smoothing local rates toward local neighboring values [84].…”

Section: Introductionmentioning

confidence: 99%

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A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping

Baptista

Mendes

MacNab

et al. 2016

Stat Methods Med Res

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Conditionally specified Gaussian Markov random field (GMRF) models with adjacency-based neighbourhood weight matrix, commonly known as neighbourhood-based GMRF models, have been the mainstream approach to spatial smoothing in Bayesian disease mapping. In the present paper, we propose a conditionally specified Gaussian random field (GRF) model with a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping. The model, named similarity-based GRF, is motivated for modelling disease mapping data in situations where the underlying small area relative risks and the associated determinant factors do not vary systematically in space, and the similarity is defined by “similarity” with respect to the associated disease determinant factors. The neighbourhood-based GMRF and the similarity-based GRF are compared and accessed via a simulation study and by two case studies, using new data on alcohol abuse in Portugal collected by the World Mental Health Survey Initiative and the well-known lip cancer data in Scotland. In the presence of disease data with no evidence of positive spatial correlation, the simulation study showed a consistent gain in efficiency from the similarity-based GRF, compared with the adjacency-based GMRF with the determinant risk factors as covariate. This new approach broadens the scope of the existing conditional autocorrelation models.

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Section: Dm As a Special Case Of Saementioning

confidence: 99%

Section: Disease Mapping Modelsmentioning

confidence: 99%

Section: Disease Mapping Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping

Baptista

Mendes

MacNab

et al. 2016

Stat Methods Med Res

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Disease Mapping, Hierarchical Models For

Waller

2012

Encyclopedia of Environmetrics

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Hierarchical models for disease mapping. Hierarchical models provide a convenient framework to combine information across multiple observational units. Such models are particularly convenient for small area estimation of disease rates to improve statistical precision of estimates associated with small local sample sizes. We review the basic framework of model‐based approaches for combining information across small administrative units through the use of random effects. Varying definitions of the random effects distribution allows one to combine information from all locations, neighboring locations, or a combination of both.

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Spatial Distribution

Waller¹

2018

Wiley StatsRef: Statistics Reference Online

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Disease Mapping

Cited by 62 publications

References 77 publications

A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping

A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping

Disease Mapping, Hierarchical Models For

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