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Title:

Hierarchical Generalized Linear Models: The R Package HGLMMM

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The R package HGLMMM has been developed to fit generalized linear models with random effects using the h-likelihood approach. The response variable is allowed to follow a binomial, Poisson, Gaussian or gamma distribution. The distribution of random effects can be specified as Gaussian, gamma, inverse-gamma or beta. Complex structures as multi-me...

The R package HGLMMM has been developed to fit generalized linear models with random effects using the h-likelihood approach. The response variable is allowed to follow a binomial, Poisson, Gaussian or gamma distribution. The distribution of random effects can be specified as Gaussian, gamma, inverse-gamma or beta. Complex structures as multi-membership design or multilevel designs can be handled. Further, dispersion parameters of random components and the residual dispersion (overdispersion) can be modeled as a function of covariates. Overdispersion parameter can be fixed or estimated. Fixed effects in the mean structure can be estimated using extended likelihood or a first order Laplace approximation to the marginal likelihood. Dispersion parameters are estimated using first order adjusted profile likelihood. Minimize

Publisher:

University of California, Los Angeles

Year of Publication:

2011-03-01T00:00:00Z

Document Type:

article

Language:

English

Subjects:

h-likelihood ; complex designs ; hierarchical generalized linear models ; overdispersion ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics

h-likelihood ; complex designs ; hierarchical generalized linear models ; overdispersion ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Statistics ; LCC:HA1-4737 ; LCC:Social Sciences ; LCC:H ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics Minimize

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Title:

Hierarchical Generalized Linear Models: The R Package HGLMMM

Description:

The R package HGLMMM has been developed to fit generalized linear models with random effects using the h-likelihood approach. The response variable is allowed to follow a binomial, Poisson, Gaussian or gamma distribution. The distribution of random effects can be specified as Gaussian, gamma, inverse-gamma or beta. Complex structures as multi-me...

The R package HGLMMM has been developed to fit generalized linear models with random effects using the h-likelihood approach. The response variable is allowed to follow a binomial, Poisson, Gaussian or gamma distribution. The distribution of random effects can be specified as Gaussian, gamma, inverse-gamma or beta. Complex structures as multi-membership design or multilevel designs can be handled. Further, dispersion parameters of random components and the residual dispersion (overdispersion) can be modeled as a function of covariates. Overdispersion parameter can be fixed or estimated. Fixed effects in the mean structure can be estimated using extended likelihood or a first order Laplace approximation to the marginal likelihood. Dispersion parameters are estimated using first order adjusted profile likelihood. Minimize

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article

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Title:

The Effect of Drop-Out on the Efficiency of Longitudinal Experiments

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article

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On the effect of the number of quadrature points in a logistic random effects model: an example

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article

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Title:

A Two-Stage Joint Model for Nonlinear Longitudinal Response and a Time-to-Event with Application in Transplantation Studies

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Publisher:

Hindawi Publishing Corporation

Year of Publication:

2012-01-01T00:00:00Z

Source:

Journal of Probability and Statistics, Vol 2012 (2012)

Journal of Probability and Statistics, Vol 2012 (2012) Minimize

Document Type:

article

Language:

English

Subjects:

LCC:Probabilities. Mathematical statistics ; LCC:QA273-280 ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Probabilities. Mathematical statistics ; LCC:QA273-280 ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Probabili...

LCC:Probabilities. Mathematical statistics ; LCC:QA273-280 ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Probabilities. Mathematical statistics ; LCC:QA273-280 ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q ; DOAJ:Statistics ; DOAJ:Mathematics and Statistics ; LCC:Probabilities. Mathematical statistics ; LCC:QA273-280 ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q ; LCC:Probabilities. Mathematical statistics ; LCC:QA273-280 ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q ; LCC:Probabilities. Mathematical statistics ; LCC:QA273-280 ; LCC:Mathematics ; LCC:QA1-939 ; LCC:Science ; LCC:Q Minimize

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http://dx.doi.org/10.1155/2012/194194

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Title:

Fully exponential Laplace approximations for the joint modelling of survival and longitudinal data

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A common objective in longitudinal studies is the joint modelling of a longitudinal response with a time-to-event outcome. Random effects are typically used in the joint modelling framework to explain the interrelationships between these two processes. However, estimation in the presence of random effects involves intractable integrals requiring...

A common objective in longitudinal studies is the joint modelling of a longitudinal response with a time-to-event outcome. Random effects are typically used in the joint modelling framework to explain the interrelationships between these two processes. However, estimation in the presence of random effects involves intractable integrals requiring numerical integration. We propose a new computational approach for fitting such models that is based on the Laplace method for integrals that makes the consideration of high dimensional random-effects structures feasible. Contrary to the standard Laplace approximation, our method requires much fewer repeated measurements per individual to produce reliable results. Copyright (c) 2009 Royal Statistical Society. Minimize

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Assessing the goodness-of-fit of the laird and ware model-an example: The Jimma infant survival differential longitudinal study

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The Jimma Infant Survival Differential Longitudinal Study is an Ethiopian study, set up to establish risk factors affecting infant survival and to investigate socio-economic, maternal and infant-rearing factors that contribute most to the child's early survival. Here, a subgroup of about 1500 children born in Jimma town is examined for their fir...

The Jimma Infant Survival Differential Longitudinal Study is an Ethiopian study, set up to establish risk factors affecting infant survival and to investigate socio-economic, maternal and infant-rearing factors that contribute most to the child's early survival. Here, a subgroup of about 1500 children born in Jimma town is examined for their first year's weight gain. Of special interest is the impact of certain cultural practices like uvulectomy, milk teeth extraction and butter swallowing, on child's weight gain; these have never been thoroughly investigated in any study. In this context, the linear mixed model (Laird and Ware) is employed. The purpose of this paper is to illustrate the practical issues when constructing the longitudinal model. Recently developed diagnostics will be used here for. Finally, special attention will be paid to the two-stage interpretation of the linear mixed model. [Ethiop.J.Health Dev. 2002;16(Special Issue):59-76] Minimize

Publisher:

Ethiopian Journal of Health Development

Year of Publication:

2002-01-01

Source:

Ethiopian Journal of Health Development; Vol 16 (2002): An Ethiopian birth cohort epidemiological study

Ethiopian Journal of Health Development; Vol 16 (2002): An Ethiopian birth cohort epidemiological study Minimize

Document Type:

Peer-reviewed Article

Language:

en

DDC:

360 Social problems & services; associations *(computed)*

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Copyright for articles published in this journal is retained by the journal.

Copyright for articles published in this journal is retained by the journal. Minimize

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Title:

A Bayesian ordinal logistic regression model to correct for interobserver measurement error in a geographical oral health study

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We present an approach for correcting for interobserver measurement error in an ordinal logistic regression model taking into account also the variability of the estimated correction terms. The different scoring behaviour of the 16 examiners complicated the identification of a geographical trend in a recent study on caries experience in Flemish ...

We present an approach for correcting for interobserver measurement error in an ordinal logistic regression model taking into account also the variability of the estimated correction terms. The different scoring behaviour of the 16 examiners complicated the identification of a geographical trend in a recent study on caries experience in Flemish children (Belgium) who were 7 years old. Since the measurement error is on the response the factor 'examiner' could be included in the regression model to correct for its confounding effect. However, controlling for examiner largely removed the geographical east-west trend. Instead, we suggest a (Bayesian) ordinal logistic model which corrects for the scoring error (compared with a gold standard) using a calibration data set. The marginal posterior distribution of the regression parameters of interest is obtained by integrating out the correction terms pertaining to the calibration data set. This is done by processing two Markov chains sequentially, whereby one Markov chain samples the correction terms. The sampled correction term is imputed in the Markov chain pertaining to the regression parameters. The model was fitted to the oral health data of the Signal-Tandmobiel-super-® study. A WinBUGS program was written to perform the analysis. Copyright 2005 Royal Statistical Society. Minimize

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Title:

a penalized Gaussian

Description:

Generalized linear mixed model with

Generalized linear mixed model with Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-08-18

Source:

http://www.karlin.mff.cuni.cz/~komarek/publications/COMSTA-3853.pdf

http://www.karlin.mff.cuni.cz/~komarek/publications/COMSTA-3853.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Key words ; Clustered data ; Logistic regression ; Longitudinal study ; Markov chain Monte Carlo ; Poisson regression

Key words ; Clustered data ; Logistic regression ; Longitudinal study ; Markov chain Monte Carlo ; Poisson regression Minimize

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

A Bayesian analysis of multivariate doubly-interval-censored dental data

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A Bayesian survival analysis is presented to examine the effect of fluoride-intake on the time to caries development of the permanent first molars in children between 7 and 12 years of age using a longitudinal study conducted in Flanders. Three problems needed to be addressed. Firstly, since the emergence time of a tooth and the time it experien...

A Bayesian survival analysis is presented to examine the effect of fluoride-intake on the time to caries development of the permanent first molars in children between 7 and 12 years of age using a longitudinal study conducted in Flanders. Three problems needed to be addressed. Firstly, since the emergence time of a tooth and the time it experiences caries were recorded yearly, the time to caries is doubly interval censored. Secondly, due to the setup of the study, many emergence times were left-censored. Thirdly, events on teeth of the same child are dependent. Our Bayesian analysis is a modified version of the intensity model of Härkänen et al. (2000, Scandinavian Journal of Statistics 27, 577–588). To tackle the problem of the large number of left-censored observations a similar Finnish data set was introduced. Our analysis shows no convincing effect of fluoride-intake on caries development. Minimize

Contributors:

The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-08-20

Source:

http://biostatistics.oxfordjournals.org/content/6/1/145.full.pdf

http://biostatistics.oxfordjournals.org/content/6/1/145.full.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Bayesian analysis ; Intensity models ; Multivariate doubly-interval-censored data. 1. RESEARCH QUESTION AND COLLECTED DATA

Bayesian analysis ; Intensity models ; Multivariate doubly-interval-censored data. 1. RESEARCH QUESTION AND COLLECTED DATA Minimize

DDC:

310 Collections of general statistics *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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