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http://hdl.handle.net/1942/21026
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DC Field | Value | Language |
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dc.contributor.author | AREGAY, Mehreteab | - |
dc.contributor.author | LAWSON, Andrew | - |
dc.contributor.author | FAES, Christel | - |
dc.contributor.author | Kirby, R.S. | - |
dc.date.accessioned | 2016-04-19T14:09:32Z | - |
dc.date.available | 2016-04-19T14:09:32Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Statistical methods in medical research 26(6), p. 2726-2742 | - |
dc.identifier.issn | 0962-2802 | - |
dc.identifier.uri | http://hdl.handle.net/1942/21026 | - |
dc.description.abstract | In disease mapping, a scale effect due to an aggregation of data from a finer resolution level to a coarser level is a common phenomenon. This article addresses this issue using a hierarchical Bayesian modeling framework. We propose four different multiscale models. The first two models use a shared random effect that the finer level inherits from the coarser level. The third model assumes two independent convolution models at the finer and coarser levels. The fourth model applies a convolution model at the finer level, but the relative risk at the coarser level is obtained by aggregating the estimates at the finer level. We compare the models using the deviance information criterion (DIC) and Watanabe-Akaike information criterion (WAIC) that are applied to real and simulated data. The results indicate that the models with shared random effects outperform the other models on a range of criteria. | - |
dc.description.sponsorship | The authors would like to acknowledge support from the National Institutes of Health via grant R01CA172805. The third author also acknowledges support from the IAP Research Network P7/06 of the Belgian State (Belgian Science Policy). | - |
dc.language.iso | en | - |
dc.subject.other | deviance information criterion (DIC); Watanabe-Akaike or widely applicable information criterion (WAIC); predictive accuracy; shared random e↵ect model; scaling effect | - |
dc.title | Bayesian multi-scale modeling for aggregated disease mapping data | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 2742 | - |
dc.identifier.issue | 6 | - |
dc.identifier.spage | 2726 | - |
dc.identifier.volume | 26 | - |
local.format.pages | 30 | - |
local.bibliographicCitation.jcat | A1 | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
local.identifier.vabb | c:vabb:394389 | - |
dc.identifier.doi | 10.1177/0962280215607546 | - |
dc.identifier.isi | 000418307900016 | - |
item.contributor | AREGAY, Mehreteab | - |
item.contributor | LAWSON, Andrew | - |
item.contributor | FAES, Christel | - |
item.contributor | Kirby, R.S. | - |
item.validation | ecoom 2019 | - |
item.validation | vabb 2017 | - |
item.fullcitation | AREGAY, Mehreteab; LAWSON, Andrew; FAES, Christel & Kirby, R.S. (2017) Bayesian multi-scale modeling for aggregated disease mapping data. In: Statistical methods in medical research 26(6), p. 2726-2742. | - |
item.accessRights | Open Access | - |
item.fulltext | With Fulltext | - |
crisitem.journal.issn | 0962-2802 | - |
crisitem.journal.eissn | 1477-0334 | - |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Aregay et al. 2015 Stat Meth Med Research.pdf | Non Peer-reviewed author version | 2.21 MB | Adobe PDF | View/Open |
nihms854137.pdf | Peer-reviewed author version | 1.04 MB | Adobe PDF | View/Open |
0962280215607546.pdf Restricted Access | Published version | 742.83 kB | Adobe PDF | View/Open Request a copy |
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