scholarly journals Bayesian Conditional Autoregressive (CAR) dengan Model Localised dalam Menaksir Risiko Relatif DBD di Kota Makassar

2020 ◽  
Vol 1 (1) ◽  
pp. 21
Author(s):  
Rusydah Khaerati ◽  
Sri Astuti Thamrin ◽  
Andi Kresna Jaya
Keyword(s):  
Relative Risk ◽  
Dengue Fever ◽  
Spatial Information ◽  
Risk Model ◽  
Fever Case ◽  
Relative Risks ◽  
Model Mapping ◽  
Conditional Autoregressive ◽  
Risk Parameters ◽  
Relative Risk Model

Bayesian Conditional Autoregressive (CAR) is used in disease mapping because it is able to model relative risks by taking into account the smoothing of the estimated relative risk and entering spatial information to reduce the errors of the estimated relative risk parameters so that a more reliable relative risk model is obtained. In this study, the relative risk value of the spread of dengue fever will be calculated using Bayesian CAR with the localised model. These results were obtained using the OpenBUGS program and are illustrated in the 2016 dengue fever case data. Based on the model, mapping of dengue fever in Makassar can be identified in each district and shows that Makassar City is still very vulnerable to dengue fever.

scholarly journals Conditional and marginal relative risk parameters for a class of recursive regression graph models

2018 ◽  
Vol 28 (10-11) ◽  
pp. 3466-3486
Author(s):  
Monia Lupparelli
Keyword(s):  
Relative Risk ◽  
Binary Data ◽  
Oral Morphine ◽  
Regression Coefficients ◽  
Relative Risks ◽  
Morphine Administration ◽  
Gaussian Case ◽  
Regression Graph ◽  
Risk Parameters ◽  
The Relationship

In linear regression modelling, the distortion of effects after marginalizing over variables of the conditioning set has been widely studied in several contexts. For Gaussian variables, the relationship between marginal and partial regression coefficients is well established and the issue is often addressed as a result of W. G. Cochran. Possible generalizations beyond the linear Gaussian case have been developed, nevertheless the case of discrete variables is still challenging, in particular in medical and social science settings. A multivariate regression framework is proposed for binary data with regression coefficients given by the logarithm of relative risks, and a multivariate Relative Risk formula is derived to define the relationship between marginal and conditional relative risks. The method is illustrated through the analysis of the morphine data in order to assess the effect of preoperative oral morphine administration on the postoperative pain relief.

Imputation for Exposure Histories with Gaps, under an Excess Relative Risk Model

Epidemiology ◽  
1996 ◽  
Vol 7 (5) ◽  
pp. 490-497 ◽  
Author(s):  
Clarice R. Weinberg ◽  
Erik S. Moledor ◽  
David M. Umbach ◽  
Dale P. Sandler
Keyword(s):  
Relative Risk ◽  
Risk Model ◽  
Excess Relative Risk ◽  
Relative Risk Model
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