Bayesian approach to analysing longitudinal bivariate binary data with informative dropout

Chan, Jennifer; Wan, Wai Yin

doi:10.1007/s00180-010-0213-5

Cited by 2 publications

(6 citation statements)

References 22 publications

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“…Chan and Wan () applied a bivariate model to describe two drug uses jointly. We adopt this approach to model jointly the heroin use and dropout process because bivariate model can better describe their association.…”

Section: The Modelsmentioning

confidence: 99%

“…() apply the method to implement the selection model. Chan and Wan () consider the Bayesian approach for the selection model with bivariate outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…To model an ID process using a selection model approach (Rubin, 1976), the conditional probability of dropout will depend on the unobserved outcome at the time of dropout. Chan and Wan (2011) adopt this selection model to the bivariate outcome model of Chan et al (1997) for two drug uses. Adopting the same bivariate model of Chan et al (1997), this paper advances the selection model to a joint model for both the probabilities of outcome and dropout so that the dependency between the two components that captures an ID process is now modeled by an odds ratio model.…”

Section: Introductionmentioning

confidence: 99%

“…Chan et al (2005) propose the method of Monte Carlo (MC) approximation using Gibbs output in the likelihood approach and Chan et al (2009) apply the method to implement the selection model. Chan and Wan (2011) consider the Bayesian approach for the selection model with bivariate outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches

Chan

2015

Biometrical J

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Dropouts are common in longitudinal study. If the dropout probability depends on the missing observations at or after dropout, this type of dropout is called informative (or nonignorable) dropout (ID). Failure to accommodate such dropout mechanism into the model will bias the parameter estimates. We propose a conditional autoregressive model for longitudinal binary data with an ID model such that the probabilities of positive outcomes as well as the drop-out indicator in each occasion are logit linear in some covariates and outcomes. This model adopting a marginal model for outcomes and a conditional model for dropouts is called a selection model. To allow for the heterogeneity and clustering effects, the outcome model is extended to incorporate mixture and random effects. Lastly, the model is further extended to a novel model that models the outcome and dropout jointly such that their dependency is formulated through an odds ratio function. Parameters are estimated by a Bayesian approach implemented using the user-friendly Bayesian software WinBUGS. A methadone clinic dataset is analyzed to illustrate the proposed models. Result shows that the treatment time effect is still significant but weaker after allowing for an ID process in the data. Finally the effect of drop-out on parameter estimates is evaluated through simulation studies.

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Section: The Modelsmentioning

confidence: 99%

“…() apply the method to implement the selection model. Chan and Wan () consider the Bayesian approach for the selection model with bivariate outcomes.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches

Chan

2015

Biometrical J

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“…Zeghnoun et al [21] assumed proportional odds, and adopted a first-order Markov model in modeling the effect of ozone on the appearance of respiratory symptoms in school children. Chan and Wan [22] proposed a bivariate binary model with a separate model for informative dropout (ID). Their model incorporates mixture and random effects.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data: Application to Insomnia Data

Ganjali¹

2012

ijsmr

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In this paper, we present a Bayesian framework for analyzing longitudinal ordinal response data. In analyzing longitudinal data, the possibility of correlations between responses given by the same individual needs to be taken into account. Various models can be used to handle such correlations such as marginal modeling, random effect modeling and transition (Markov) modeling. Here a transition modeling is used and a Bayesian approach is presented for analyzing longitudinal data. A cumulative logistic regression model and the Bayesian method, using MCMC, are implemented for obtaining the parameters estimates. Our approach is applied on a two-period longitudinal Insomnia data where the Bayesian estimate for measure of association, , between the initial and follow-up ordinal responses is obtained in each level of a treatment variable. Then, the sensitivity of posterior summaries to changes of prior hyperparameters is investigated. We also use Bayes factor criterion for testing some important hypotheses.

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Bayesian approach to analysing longitudinal bivariate binary data with informative dropout

Abstract: Correlated bivariate binary data, Informative dropout, Random effects model, Bayesian method, Markov chain Monte Carlo method,

Cited by 2 publications

References 22 publications

Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches

Bayesian informative dropout model for longitudinal binary data with random effects using conditional and joint modeling approaches

Bayesian Analysis of Transition Model for Longitudinal Ordinal Response Data: Application to Insomnia Data

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