Empirical bayes versus fully bayesian analysis of geographical variation in disease risk

1992 ◽  
Vol 11 (8) ◽  
pp. 983-1007 ◽  
Author(s):  
Luisa Bernardinelli ◽  
Cristina Montomoli
Keyword(s):  
Bayesian Analysis ◽  
Empirical Bayes ◽  
Geographical Variation ◽  
Disease Risk ◽  
Fully Bayesian

Bayesian Analysis of Geographical Variation in Disease Risk

2005 ◽  
pp. 222-228
Author(s):  
C. L. Vidal-Rodeiro ◽  
M. I. Santiago-Perez ◽  
E. Vazquez-Fernandez ◽  
M. E. Lopez-Vizcaino ◽  
X. Hervada-Vidal
Keyword(s):  
Public Health ◽  
Bayesian Analysis ◽  
Geographical Distribution ◽  
Geographical Variation ◽  
Disease Risk ◽  
Disease Mapping

Disease mapping is a big focus of interest in the area of public health (Bailey, 2001; Moore & Carpenter, 1999), and the geographical distribution of a disease has an important role in understanding its origin, its causes or its evolution.

scholarly journals Fully Bayesian analysis of allele-specific RNA-seq data

2019 ◽  
Vol 16 (6) ◽  
pp. 7751-7770 ◽  
Author(s):  
Ignacio Alvarez-Castro ◽  
◽  
Jarad Niemi ◽  
Keyword(s):  
Bayesian Analysis ◽  
Rna Seq ◽  
Allele Specific ◽  
Fully Bayesian

Analysing crack growth

1998 ◽  
Vol 212 (3) ◽  
pp. 157-166 ◽  
Author(s):  
M Newby
Keyword(s):  
Bayesian Analysis ◽  
Crack Length ◽  
Crack Growth ◽  
Failure Time ◽  
Growth Models ◽  
Accelerated Failure Time ◽  
Deterministic Models ◽  
Stochastic Growth Models ◽  
Standard Linear ◽  
Fully Bayesian

Deterministic models of crack growth can be fitted to experimental data. This paper shows that stochastic growth models are easy to use and provides a simple framework for data analysis. A simple transformation allows the standard linear regression model to be used and opens the way for a fully Bayesian analysis. The Bayesian analysis allows the incorporation of prior information and coherent predictions of crack length to be made. The parameters of the Paris-Erdogan model are readily evaluated directly from crack length data without the need for intermediate estimates of the crack growth rate. The approach lends itself to the analysis of properly designed experiments to determine the effect of environmental factors on the parameters of the Paris-Erdogan equation through the medium of accelerated failure time models. The paper also emphasizes the need for adequate communication between experimenter and statistician to ensure efficient experimental designs.

scholarly journals On estimation the relative risk of small area and visualization spatio-temporal map

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoxiao Song ◽  
Yan Li ◽  
Le Cai ◽  
Wei Liu ◽  
Wenlong Cui
Keyword(s):  
Relative Risk ◽  
Small Area ◽  
Empirical Bayes ◽  
Disease Risk ◽  
Risk Estimation ◽  
Risk Index ◽  
Disease Mapping ◽  
R Software ◽  
Relative Risks ◽  
Spatio Temporal

ObjectiveThe purpose is to propose a serial of approach for estimation for disease risk for ILI in "small area" and present the risk values by spatio-temporal disease mapping or an interactive visualization with HTML format.IntroductionDisease mapping is a method used to descript the geographical variation in risk (heterogeneity of risk) and to provide the potential reason (factors or confounders) to explain the distribution. Possibly the most famous uses of disease mapping in epidemiology were the studies by John Snow of the cholera epidemics in London. Accurate estimation relative risk of small areas such as mortality and morbidity, by different age, ethnic group, interval and regions, is important for government agencies to identify hazards and mitigate disease burden. Recently, as the innovative algorithms and the available software, more and more disease risk index has been pouring out. This abstract will provide several estimation risk index, from raw incidence to model-based relative risks, and use visual approach to display them.MethodsAll the data are from a syndromic surveillance and real-time early warning system in the Yunnan province in the China. For brief introduction aim, we are using the ILI (Influenza-like illness) data in December 2017 in one county. The relative risks of disease in small area are including: raw incidence, a standardized morbidity ratio (SMR), Empirical Bayes smoothing estimation relative risk (EB-RR) and the Besag-York-Mollio model (BYM). The incidence in each small area is common used for descriptive the risk but fail to comparable directly since the different population at risk in each area. SMR is a good way to deal with this incomparability. But SMR can give rise to imprecisely estimate in areas with small populations. Empirical Bayes estimation approach has been used for smoothing purpose and can be seen as a compromise between relative risks and P-values. However, all above approaches are inept to have spatial or spatio-temporal structure in mind. BYM based the Bayesian inference can handle both the area-specific spatial structured component (such as intrinsic conditional autoregressive component) and the exchangeable random effect (unstructured component). All the analyses are implemented in the R software with INLA package (http://www.r-inla.org). The outcome of relative risk estimation with visual way and interactive maps showing are using ggplot2 and leaflet packages.Results1, the spatio-temporal raw cases of ILI from 2017/12/01 to 2017/12/31 is Fig.12. the SMR and EB-RR estimation RR of ILI are in Fig.2 and Fig.33. the most excited is the interactive visualization with HTML format for all the risk indexes is visited http://rpubs.com/ynsxx/424814 in detail. And the screenshot is Fig.4ConclusionsSmall area disease risk estimation is important for disease prevention and control. The faster function of computer with power R software can lead to advance in disease mapping, allowing for complex spatio-temporal models and communicate the results with visualization way. 

scholarly journals An Analysis of Cepheid Distances Using Bayesian Statistics

2000 ◽  
Vol 176 ◽  
pp. 228-228
Author(s):  
Thomas G. Barnes ◽  
William H. Jefferys
Keyword(s):  
Bayesian Analysis ◽  
Surface Brightness ◽  
Radial Velocity Curve ◽  
Objective Model ◽  
Likelihood Approach ◽  
Selection For ◽  
Absolute Magnitudes ◽  
The Individual ◽  
Fully Bayesian ◽  
Selection Of

We have applied an approximately Bayesian and a fully Bayesian analysis to the calculation of Cepheid distances, radii and absolute magnitudes using the surface brightness (Baade–Wesselink) method. Both methods successfully account for errors in the data, provide unbiased distance estimates, and provide objective model selection for the radial velocity curve. In addition, the fully Bayesian analysis objectively selects a model for the magnitude curve; averages over models of various Fourier orders, properly weighted by the posterior probabilities of the individual models; and includes a Lutz–Kelker correction.The approximately Bayesian method is that described by Jefferys & Barnes (1999) and Barnes & Jefferys (1999). It is a maximum likelihood approach with objective selection of the order of the Fourier series model of the radial velocities.

scholarly journals Evaluation of the uptake and delivery of the NHS Health Check programme in England, using primary care data from 9.5 million people: a cross-sectional study

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e042963
Author(s):  
Riyaz Patel ◽  
Sharmani Barnard ◽  
Katherine Thompson ◽  
Catherine Lagord ◽  
Emma Clegg ◽  
...  
Keyword(s):  
Risk Factors ◽  
Primary Care ◽  
Geographical Variation ◽  
Disease Risk ◽  
Communicable Disease ◽  
Behavioural Change ◽  
Health Check ◽  
Cvd Risk ◽  
Primary Care Data ◽  
National Analysis

ObjectivesTo describe the uptake and outputs of the National Health Service Health Check (NHSHC) programme in England.DesignObservational study.SettingNational primary care data extracted directly by NHS Digital from 90% of general practices (GP) in England.ParticipantsIndividuals aged 40–74 years, invited to or completing a NHSHC between 2012 and 2017, defined using primary care Read codes.InterventionThe NHSHC, a structured assessment of non-communicable disease risk factors and 10-year cardiovascular disease (CVD) risk, with recommendations for behavioural change support and therapeutic interventions.ResultsDuring the 5-year cycle, 9 694 979 individuals were offered an NHSHC and 5 102 758 (52.6%) took up the offer. There was geographical variation in uptake between local authorities across England ranging from 25.1% to 84.7%. Invitation methods changed over time to incorporate greater digitalisation, opportunistic delivery and delivery by third-party providers.The population offered an NHSHC resembled the English population in ethnicity and deprivation characteristics. Attendees were more likely to be older and women, but were similar in terms of ethnicity and deprivation, compared with non-attendees. Among attendees, risk factor prevalence reflected population survey estimates for England. Where a CVD risk score was documented, 25.9% had a 10-year CVD risk ≥10%, of which 20.3% were prescribed a statin. Advice, information and referrals were coded as delivered to over 2.5 million individuals identified to have risk factors.ConclusionThis national analysis of the NHSHC programme, using primary care data from over 9.5 million individuals offered a check, reveals an uptake rate of over 50% and no significant evidence of inequity by ethnicity or deprivation. To maximise the anticipated value of the NHSHC, we suggest continued action is needed to invite more eligible people for a check, reduce geographical variation in uptake, prioritise engagement with non-attendees and promote greater use of evidence-based interventions especially where risk is identified.

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