A random-censoring Poisson model for underreported data.
A major challenge when monitoring risks in socially deprived areas of under developed countries is that economic, epidemiological, and social data are typically underreported. Thus, statistical models that do not take the data quality into account will produce biased estimates. To deal with this pro...
| Publicado en: | Statistics in Medicine Vol. 36; no. 30; pp. 4873 - 4893 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
12/30/2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=126563371&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 126563371 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02776715 2DZ jtl: Statistics in Medicine issn: 02776715 maglogo: Y pubinfo: dt: 12/30/2017 vid: 36 iid: 30 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 126563371 126563371 NLM29067731 126563371 10.1002/sim.7456 NLM29067731 126563371 ppf: 4873 ppct: 20 formats: tig: atl: A random-censoring Poisson model for underreported data. aug: au: Oliveira, Guilherme Lopes Loschi, Rosangela Helena Assunção, Renato Martins de Oliveira, Guilherme Lopes Assunção, Renato Martins affil: Departamento de Estatística, Universidade Federal de Minas Gerais, Av. Antônio Carlos, 6.627, Belo Horizonte Minas Gerais, 31270‐901, Brazil sug: subj: Poisson Distribution Models, Statistical Probability Algorithms Infant Statistics Infant Mortality Systems Analysis Infant, Newborn Computer Simulation Brazil Human Infant: 1-23 months Infant, Newborn: birth-1 month ab: A major challenge when monitoring risks in socially deprived areas of under developed countries is that economic, epidemiological, and social data are typically underreported. Thus, statistical models that do not take the data quality into account will produce biased estimates. To deal with this problem, counts in suspected regions are usually approached as censored information. The censored Poisson model can be considered, but all censored regions must be precisely known a priori, which is not a reasonable assumption in most practical situations. We introduce the random-censoring Poisson model (RCPM) which accounts for the uncertainty about both the count and the data reporting processes. Consequently, for each region, we will be able to estimate the relative risk for the event of interest as well as the censoring probability. To facilitate the posterior sampling process, we propose a Markov chain Monte Carlo scheme based on the data augmentation technique. We run a simulation study comparing the proposed RCPM with 2 competitive models. Different scenarios are considered. RCPM and censored Poisson model are applied to account for potential underreporting of early neonatal mortality counts in regions of Minas Gerais State, Brazil, where data quality is known to be poor. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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