Handling non-ignorable dropouts in longitudinal data: a conditional model based on a latent Markov heterogeneity structure

  title={Handling non-ignorable dropouts in longitudinal data: a conditional model based on a latent Markov heterogeneity structure},
  author={Antonello Maruotti},
We illustrate a class of conditional models for the analysis of longitudinal data suffering attrition in random effects models framework, where the subject-specific random effects are assumed to be discrete and to follow a time-dependent latent process. The latent process accounts for unobserved heterogeneity and correlation between individuals in a dynamic fashion, and for dependence between the observed process and the missing data mechanism. Of particular interest is the case where the… 
Latent drop-out based transitions in linear quantile hidden Markov models for longitudinal responses with attrition
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Finite Mixtures of Hidden Markov Models for Longitudinal Responses Subject to Drop out
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Latent drop-out transitions in quantile regression
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Maximum likelihood estimation of hidden Markov models for continuous longitudinal data with missing responses and dropout
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A shared-parameter approach for jointly modeling longitudinal and survival data allows for time-varying random effects that affect both the longitudinal and the survival processes.
Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values
A finite mixture of multivariate t nonlinear mixed model is proposed, and this new model allows accommodating more complex features of longitudinal data.
A mixed-effects estimating equation approach to nonignorable missing longitudinal data with Refreshment samples
Nonignorable missing data occur frequently in longitudinal studies and can cause biased estimations. Refreshment samples which draw new subjects randomly in subsequent waves from the original
Bayesian Diagnostics of Hidden Markov Structural Equation Models with Missing Data
A Bayesian local influence procedure is developed for HMMs with latent variables in the presence of missing data to investigate the dynamic heterogeneity of multivariate longitudinal data, reveal how the interrelationships among latent variables change from one state to another, and simultaneously conduct statistical diagnosis for the given data, model assumptions, and prior inputs.


Generalized linear mixed joint model for longitudinal and survival outcomes
This paper formulate an approach for non-Gaussian longitudinal outcomes in the framework of joint models, and assumes that the history of the response up to current time may have an influence on the risk of dropout.
A selection model for longitudinal binary responses subject to non-ignorable attrition.
A selection model extending semiparametric variance component models for longitudinal binary responses to allow for dependence between the missing data mechanism and the primary response process is proposed.
A semiparametric approach to hidden Markov models under longitudinal observations
A hidden Markov model for longitudinal count data where sources of unobserved heterogeneity arise, making data overdispersed is proposed, using a more flexible approach based on the Expectation Maximization (EM) algorithm.
Random coefficient models for binary longitudinal responses with attrition
A class of conditional models are proposed to deal with binary longitudinal responses, including unknown sources of heterogeneity in the regression parameters as well as serial dependence of Markovian form, estimated by means of an EM algorithm for nonparametric maximum likelihood.
A two‐part mixed‐effects pattern‐mixture model to handle zero‐inflation and incompleteness in a longitudinal setting
  • A. Maruotti
  • Computer Science
    Biometrical journal. Biometrische Zeitschrift
  • 2011
A finite mixture of hurdle models are proposed to face the heterogeneity problem, which is handled by introducing random effects with a discrete distribution; a pattern‐mixture approach is specified to deal with non‐ignorable missing values.
An approximate generalized linear model with random effects for informative missing data.
This paper develops a class of models to deal with missing data from longitudinal studies that allow the primary response, conditional on the random parameter, to follow a generalizedlinear model and approximate the generalized linear model by conditioning on the data that describes missingness.
A latent-class mixture model for incomplete longitudinal Gaussian data.
It is argued that analyses valid under MNAR are not well suited for the primary analysis in clinical trials, and one route for sensitivity analysis is to consider, next to selection models, pattern-mixture models or shared-parameter models, a latent-class mixture model.
Random effects and latent processes approaches for analyzing binary longitudinal data with missingness: a comparison of approaches using opiate clinical trial data
Various random effects and latent process models which have been proposed for analyzing longitudinal binary data subject to both non-ignorable intermittent missing data and dropout are discussed.
A comparison of some criteria for states selection in the latent Markov model for longitudinal data
The results of a Monte Carlo simulation study aimed at comparing the performance of the above states selection criteria on the basis of a wide set of model specifications are shown.
Longitudinal data with dropout : objectives, assumptions and a proposal (with Discussion)
It is argued that more attention could be given to study objectives and to the relevant targets for inference in the analysis of longitudinal data that are complicated by possibly informative drop-out, and a new and computationally efficient modelling and analysis procedure is proposed.