Trends in epidemiology in the 21st century: time to adopt Bayesian methods

Martinez, Edson Zangiacomi; Achcar, Jorge Alberto

doi:10.1590/0102-311x00144013

Cited by 14 publications

(11 citation statements)

References 66 publications

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“…Frequentist inference assumes that the parameter of interest is an objective number representing a frequency in an infinite number of trials, whereas in the Bayesian approach the parameter reflects a measure of personal belief, that is, subjective knowledge. 7 Bayes's theorem provides a mechanism for updating knowledge when new evidence is collected. While regression models can be implemented in a Bayesian framework, BNs generate their estimates by calculations of the conditional probability distribution over a variable of interest given observations of other variables.…”

Section: Bayesian Networkmentioning

confidence: 99%

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine

Arora

Boyne

Slater³

et al. 2019

Value in Health

110

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Objective: The fields of medicine and public health are undergoing a data revolution. An increasing availability of data has brought about a growing interest in machine-learning algorithms. Our objective is to present the reader with an introduction to a knowledge representation and machine-learning tool for risk estimation in medical science known as Bayesian networks (BNs). Study Design: In this article we review how BNs are compact and intuitive graphical representations of joint probability distributions (JPDs) that can be used to conduct causal reasoning and risk estimation analysis and offer several advantages over regression-based methods. We discuss how BNs represent a different approach to risk estimation in that they are graphical representations of JPDs that take the form of a network representing model random variables and the influences between them, respectively. Methods: We explore some of the challenges associated with traditional risk prediction methods and then describe BNs, their construction, application, and advantages in risk prediction based on examples in cancer and heart disease.Results: Risk modeling with BNs has advantages over regressionbased approaches, and in this article we focus on three that are relevant to health outcomes research: (1) the generation of network structures in which relationships between variables can be easily communicated; (2) their ability to apply Bayes's theorem to conduct individual-level risk estimation; and (3) their easy transformation into decision models. Conclusions: Bayesian networks represent a powerful and flexible tool for the analysis of health economics and outcomes research data in the era of precision medicine.

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Section: Bayesian Networkmentioning

confidence: 99%

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine

Arora

Boyne

Slater³

et al. 2019

Value in Health

110

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“…Esses modelos foram utilizados considerando que os escores do P-DUREL são representados por variá-veis quantitativas discretas que assumem um número finito de possibilidades, o que torna a distribuição beta-binomial adequada aos dados da escala 39 . Estimativas dos coeficientes dos modelos e seus respectivos valores de IC95% foram obtidos por métodos de inferência bayesiana 40 com distribuições, a priori, não informativas e com o auxílio de simulação Monte Carlo em Cadeia de Markov (MCMC). Os intervalos de credibilidade bayesianos são análogos aos intervalos de confiança obtidos pelo método frequentista de estatís-tica.…”

Section: Análise Estatísticaunclassified

Investigação das propriedades psicométricas do Duke Religious Index no âmbito da pesquisa em Saúde Coletiva

Martínez¹,

Alves²,

Carneiro³

et al. 2014

Cad. saúde colet.

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Cad. Saúde Colet., 2014, Rio de Janeiro, 22 (4): 419-27 Resumo A religiosidade é uma variável importante para a pesquisa em saúde, considerando suas conhecidas associações com o processo saúde-doença e questões importantes sobre o bem-estar, atitudes, práticas e satisfação em relação a cuidados e serviços. No entanto, na pesquisa em Saúde Coletiva, é frequentemente usada a afiliação religiosa como única dimensão da religiosidade. O presente estudo objetivou investigar a adequação do uso do instrumento Duke Religious Index (DUREL) no âmbito da pesquisa em Saúde Coletiva, ao avaliar as capacidades psicométricas do instrumento quando aplicado a uma população representativa de usuários dos serviços de atenção primária à saúde de um município de porte médio do interior do Estado de São Paulo. Foi encontrada uma adequada consistência interna para o instrumento e validade convergente-discriminante. Os resultados sugerem que o uso do DUREL não é restrito à pesquisa em saúde mental e que sua utilização pode trazer novas perspectivas para o entendimento de eventos de interesse na área da Saúde Coletiva.Palavras-chave: religião e medicina; questionários; estudos de validação. AbstractReligiosity is an important variable in health research, given its known association with the health-disease process and important questions about the well-being, attitudes, practices and satisfaction with care and services. However, in the research in Public Health, it is often used religious affiliation as the only dimension of religiosity. This study aims to investigate the appropriateness of using the Duke Religious Index scale (DUREL) in the context of the Public Health research, evaluating the psychometric capabilities of the instrument when applied to a representative sample of users of primary care services to health in a medium-sized city of the interior of São Paulo State, Southeast Brazil. It was found an adequate internal consistency for the instrument and satisfactory convergent-discriminant validity. The results suggest that the use of DUREL is not restricted to research in mental health, and that its use can bring new perspectives for the understanding of events of interest in the field of Public Health.

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“…Bayesian methods are usually considered to analyse spatial data assuming models with random effects expressed by a CAR structure, mostly because of the difficulty in applying frequentist statistical techniques due to the complexity of the likelihood function. 33 We tried other neighborhood structures, 34 such as k-nearest structures, but we have not substantial differences. From the results of this study, it was concluded that the proposed model shows to be useful to model associations between socioeconomic aspects of microregions and the pseudo-compositional data.…”

Section: Discussionmentioning

confidence: 99%

A Bayesian analysis for pseudo-compositional data with spatial structure

Martinez

Achcar

Aragon

et al. 2019

Stat Methods Med Res

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We proposed a Bayesian analysis of pseudo-compositional data in presence of a latent factor, assuming a spatial structure. This development was motivated by a dataset containing information on the number of newborns of primiparous mothers living in each of the microregions of the state of Sao Paulo, Brazil, in the year of 2015, stratified by the age of the mothers (15–18, 19–29 and 30 years or more). Considering that data on newborns are not stochastically distributed among the three age groups, but they are explained in relation to women's population structure, we adopted the expression “pseudo-compositional data” to refer to this data structure. The hypothesis of interest establishes that the age of the first pregnancy is associated with the economic conditions of the geographic area where the mother lives. The incidence of poverty was included as an independent variable. Additive log-ratio ( alr) and isometric log-ratio ( ilr) transformations were considered, as is usually done in the analysis of compositional data. The model included a random effect related to the spatial effect assumed to have a conditional autoregressive structure. A Bayesian Markov Chain Monte Carlo (MCMC) simulation procedure was used to get the posterior summaries of interest. The model based on the ( ilr) transformation was well fitted to the data, showing that in the microregions with the highest incidence of poverty, there are higher proportions of women who have their first child in adolescence, while in the microregions with the lowest incidence of poverty, there are higher proportions of women who have their first child after the age of 30 years. From these results it is possible to conclude that this Bayesian approach was very useful in the estimation of the parameters of the proposed model. The proposed method should have a broad application to other problems involving pseudo-compositional data.

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Trends in epidemiology in the 21st century: time to adopt Bayesian methods

Abstract: Trends in epidemiology in the 21st century: time to adopt Bayesian methods Tendências da epidemiologia no século XXI: é o tempo dos métodos bayesianos Tendencias de la epidemiología del siglo XXI: es tiempo para los métodos bayesianos

Cited by 14 publications

References 66 publications

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine

Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine

Investigação das propriedades psicométricas do Duke Religious Index no âmbito da pesquisa em Saúde Coletiva

A Bayesian analysis for pseudo-compositional data with spatial structure

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