Estimating infant mortality in Colombia: some overdispersion modelling approaches

Quintero-Sarmiento, Adrián; Cepeda-Cuervo, Edilberto; Núñez‐Antón, Vicente

doi:10.1080/02664763.2011.634395

Cited by 12 publications

(24 citation statements)

References 22 publications

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“…Generalized overdispersion models for count data, in which regression structures are assumed for both mean and overdispersion parameters, may be an appropriate alternative to analyze count area data, where spatial association between observations is present. 14 However, given that these models do not provide information related to the strength of the spatial association between observations of the variable under study (e.g. in order to be able to plan and develop local, regional or national policies of prevention in the case of infant mortality studies), some more general models should be specified.…”

Section: Spatial Conditional Overdispersion Modelsmentioning

confidence: 99%

“…These models assume that the conditional overdispersion density functions (equation (1)) follow either a Poisson or a binomial distribution. We now briefly describe some of the previous proposals related both to the Poisson and binomial distributions 14 that will lead to our proposal of the generalized spatial conditional overdispersed models in Section 2.3.…”

Section: Spatial Conditional Overdispersion Modelsmentioning

confidence: 99%

“…infant mortality rates), and that there are also some socioeconomic and sociocultural characteristics, health services and other additional variables that do not change from one observational unit to another, there is a clear motivation for analyzing this type of data set with the use of overdispersion models, such as the negative binomial or Poisson normal regression models, 5 or the generalized overdispersion models. 14…”

Section: Spatial Conditional Overdispersion Modelsmentioning

confidence: 99%

“…Another possible model for overdispersed data is the generalized conditional normal Poisson model. 14 We propose an extension of this model, so that it allows for modeling spatial neighborhood structures. That is, we assume that (Yi|νi) follows a Poisson distribution with parameter μi,i=1,…,n, where νi ∼N(0,σi 2).…”

Section: Spatial Conditional Overdispersion Modelsmentioning

confidence: 99%

“…unsatisfied Basic Needs) affecting infant mortality rates and female postnatal screening by considering several alternative and innovative approaches. 14 Moreover, they also proposed some overdispersion model extensions that included a joint modeling of the mean and overdispersion parameters.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Conditional overdispersed models: Application to count area data

Cepeda-Cuervo

Córdoba

Núñez‐Antón

2017

Stat Methods Med Res

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This paper proposes alternative models for the analysis of count data featuring a given spatial structure, which corresponds to geographical areas. We assume that the overdispersion data structure partially results from the existing and well justified spatial correlation between geographical adjacent regions, so an extension of existing overdispersion models that include spatial neighborhood structures within a Bayesian framework is proposed. These models allow practitioners to quantify the association explained by the considered neighborhood structures and the one modelled by additional factors. Finally, using the information provided by the Colombian National Demographic and Health Survey, the usefulness of the proposed models is illustrated by fitting them to infant mortality rates and to data including the proportion of mothers who, after giving birth to their last child, underwent a postnatal screening period in Colombia.

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Section: Spatial Conditional Overdispersion Modelsmentioning

confidence: 99%

Section: Spatial Conditional Overdispersion Modelsmentioning

confidence: 99%

Section: Spatial Conditional Overdispersion Modelsmentioning

confidence: 99%

Section: Spatial Conditional Overdispersion Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Conditional overdispersed models: Application to count area data

Cepeda-Cuervo

Córdoba

Núñez‐Antón

2017

Stat Methods Med Res

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Multilevel covariance regression with correlated random effects in the mean and variance structure

Quintero

2017

Biometrical J

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Multivariate regression methods generally assume a constant covariance matrix for the observations. In case a heteroscedastic model is needed, the parametric and nonparametric covariance regression approaches can be restrictive in the literature. We propose a multilevel regression model for the mean and covariance structure, including random intercepts in both components and allowing for correlation between them. The implied conditional covariance function can be different across clusters as a result of the random effect in the variance structure. In addition, allowing for correlation between the random intercepts in the mean and covariance makes the model convenient for skewedly distributed responses. Furthermore, it permits us to analyse directly the relation between the mean response level and the variability in each cluster. Parameter estimation is carried out via Gibbs sampling. We compare the performance of our model to other covariance modelling approaches in a simulation study. Finally, the proposed model is applied to the RN4CAST dataset to identify the variables that impact burnout of nurses in Belgium.

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Comparing hierarchical models via the marginalized deviance information criterion

Quintero

2018

Statistics in Medicine

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Hierarchical models are extensively used in pharmacokinetics and longitudinal studies. When the estimation is performed from a Bayesian approach, model comparison is often based on the deviance information criterion (DIC). In hierarchical models with latent variables, there are several versions of this statistic: the conditional DIC (cDIC) that incorporates the latent variables in the focus of the analysis and the marginalized DIC (mDIC) that integrates them out. Regardless of the asymptotic and coherency difficulties of cDIC, this alternative is usually used in Markov chain Monte Carlo (MCMC) methods for hierarchical models because of practical convenience. The mDIC criterion is more appropriate in most cases but requires integration of the likelihood, which is computationally demanding and not implemented in Bayesian software. Therefore, we consider a method to compute mDIC by generating replicate samples of the latent variables that need to be integrated out. This alternative can be easily conducted from the MCMC output of Bayesian packages and is widely applicable to hierarchical models in general. Additionally, we propose some approximations in order to reduce the computational complexity for large-sample situations. The method is illustrated with simulated data sets and 2 medical studies, evidencing that cDIC may be misleading whilst mDIC appears pertinent.

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Estimating infant mortality in Colombia: some overdispersion modelling approaches

Cited by 12 publications

References 22 publications

Conditional overdispersed models: Application to count area data

Conditional overdispersed models: Application to count area data

Multilevel covariance regression with correlated random effects in the mean and variance structure

Comparing hierarchical models via the marginalized deviance information criterion

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