Bayesian estimates of disease maps: How important are priors?

Bernardinelli, Luisa; Clayton, David; Montomoli, Cristina

doi:10.1002/sim.4780142111

Cited by 267 publications

(176 citation statements)

References 19 publications

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“…A hierarchical Bayesian approach is used to filter out the Poisson variation from the map and obtain maps which better reflect the true heterogeneity of the RR (Bernardinelli et al, 1995a). Bayesian methodology uses predefined information about the parameter of interest via so called prior distribution and combines it with information available from observed data and produces posterior distribution of the parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Bayesian methodology uses predefined information about the parameter of interest via so called prior distribution and combines it with information available from observed data and produces posterior distribution of the parameters. The mean of this distribution is called the Bayesian estimates and tends to be less dispersed (Bernardinelli et al, 1995a). Clayton and Kaldor (1987) applied empirical Bayesian method to estimate incidence of lip cancer in Scotland.…”

Section: Introductionmentioning

confidence: 99%

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Trends of Breast Cancer Incidence in Iran During 2004-2008: A Bayesian Space-time Model

Jafari‐Koshki

Schmid²,

Mahaki³

2014

Asian Pacific Journal of Cancer Prevention

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Background: Breast cancer is the most frequently diagnosed cancer in women and estimating its relative risks and trends of incidence at the area-level is helpful for health policy makers. However, traditional methods of estimation which do not take spatial heterogeneity into account suffer from drawbacks and their results may be misleading, as the estimated maps of incidence vary dramatically in neighboring areas. Spatial methods have been proposed to overcome drawbacks of traditional methods by including spatial sources of variation in the model to produce smoother maps. Materials and Methods: In this study we analyzed the breast cancer data in Iran during 2004-2008. We used a method proposed to cover spatial and temporal effects simultaneously and their interactions to study trends of breast cancer incidence in Iran. Results: The results agree with previous studies but provide new information about two main issues regarding the trend of breast cancer in provinces of Iran. First, this model discovered provinces with high relative risks of breast cancer during the 5 years of the study. Second, new information was provided with respect to overall trend trends o. East-Azerbaijan, Golestan, North-Khorasan, and Khorasan-Razavi had the highest increases in rates of breast cancer incidence whilst Tehran, Isfahan, and Yazd had the highest incidence rates during 2004-2008. Conclusions: Using spatial methods can provide more accurate and detailed information about the incidence or prevalence of a disease. These models can specify provinces with different health priorities in terms of needs for therapy and drugs or demands for efficient education, screening, and preventive policy into action.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trends of Breast Cancer Incidence in Iran During 2004-2008: A Bayesian Space-time Model

Jafari‐Koshki

Schmid²,

Mahaki³

2014

Asian Pacific Journal of Cancer Prevention

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“…In (2), we observe that β 0j , β 1j , β 2j , β 3j , β 4j , and β 5j are fixed effect regression parameters associated with the covariates X 1 , X 2 , X 3 , X 4 , and X 5 ; i=1,…, 9 and j=1, ….,10. b i is a random effect that captures the possible correlations among the malaria counts, taking into account the region effects of neighboring provinces assumed to have a normal CAR structure [15][16][17][18][19][20][21] model, that is,…”

Section: The Statistical Modelmentioning

confidence: 99%

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

Achcar

Martinez

Souza

et al. 2011

Rev. Soc. Bras. Med. Trop.

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Introduction: Malaria is a serious problem in the Brazilian Amazon region, and the detection of possible risk factors could be of great interest for public health authorities. The objective of this article was to investigate the association between environmental variables and the yearly registers of malaria in the Amazon region using Bayesian spatiotemporal methods. Methods: We used Poisson spatiotemporal regression models to analyze the Brazilian Amazon forest malaria count for the period from 1999 to 2008. In this study, we included some covariates that could be important in the yearly prediction of malaria, such as deforestation rate. We obtained the inferences using a Bayesian approach and Markov Chain Monte Carlo (MCMC) methods to simulate samples for the joint posterior distribution of interest. The discrimination of different models was also discussed. Results: The model proposed here suggests that deforestation rate, the number of inhabitants per km 2 , and the human development index (HDI) are important in the prediction of malaria cases. Conclusions: It is possible to conclude that human development, population growth, deforestation, and their associated ecological alterations are conducive to increasing malaria risk. We conclude that the use of Poisson regression models that capture the spatial and temporal effects under the Bayesian paradigm is a good strategy for modeling malaria counts.

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“…A special issue devoted to the growing area of spatial disease patterns illustrated the impact of powerful flexible Bayesian modelling in smoothing, modelling heterogeneity and clustering and accounting for temporal as well as spatial trends [232][233][234][235][236][237]. Gibbs sampling enabled the modelling of population risk in meta-analysis, avoiding the problems of bias due to measurement error [238], and the incorporation of external trials to the meta-analysis to better estimate the heterogeneity [239].…”

Section: Markov Chain Monte Carlomentioning

confidence: 99%

Bayesian statistics in medicine: a 25 year review

2006

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SUMMARYThis review examines the state of Bayesian thinking as Statistics in Medicine was launched in 1982, reflecting particularly on its applicability and uses in medical research. It then looks at each subsequent five-year epoch, with a focus on papers appearing in Statistics in Medicine, putting these in the context of major developments in Bayesian thinking and computation with reference to important books, landmark meetings and seminal papers. It charts the growth of Bayesian statistics as it is applied to medicine and makes predictions for the future. From sparse beginnings, where Bayesian statistics was barely mentioned, Bayesian statistics has now permeated all the major areas of medical statistics, including clinical trials, epidemiology, meta-analyses and evidence synthesis, spatial modelling, longitudinal modelling, survival modelling, molecular genetics and decision-making in respect of new technologies.

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Bayesian estimates of disease maps: How important are priors?

Cited by 267 publications

References 19 publications

Trends of Breast Cancer Incidence in Iran During 2004-2008: A Bayesian Space-time Model

Trends of Breast Cancer Incidence in Iran During 2004-2008: A Bayesian Space-time Model

Use of Poisson spatiotemporal regression models for the Brazilian Amazon Forest: malaria count data

Bayesian statistics in medicine: a 25 year review

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