The analysis of longitudinal ordinal data with nonrandom drop-out
From MaRDI portal
Publication:4364840
DOI10.1093/biomet/84.1.33zbMath0883.62120OpenAlexW2054353235MaRDI QIDQ4364840
No author found.
Publication date: 9 March 1998
Published in: Biometrika (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1093/biomet/84.1.33
EM algorithmrepeated measurementsmarginal modelmissing valuesglobal odds ratioDale modelfluvoxamine study
Applications of statistics to biology and medical sciences; meta analysis (62P10) Linear inference, regression (62J99)
Related Items (44)
Skew-mixed effects model for multivariate longitudinal data with categorical outcomes and missingness ⋮ A weighted simulation-based estimator for incomplete longitudinal data models ⋮ Fitting time series models for longitudinal surveys with nonignorable missing data ⋮ Simultaneous Bayesian modelling of skew-normal longitudinal measurements with non-ignorable dropout ⋮ A comparison of various software tools for dealing with missing data via imputation ⋮ The analysis of ordered categorical data: An overview and a survey of recent developments. (With discussion) ⋮ A discrete time event‐history approach to informative drop‐out in mixed latent Markov models with covariates ⋮ Bayesian analysis of Turkish Income and Living Conditions data, using clustered longitudinal ordinal modelling with Bridge distributed random effects ⋮ Modeling Clustered Ordered Categorical Data: A Survey ⋮ Binomial proportion estimation in longitudinal data with non-ignorable non-response ⋮ Modeling omitted and not-reached items in IRT models ⋮ Missing data methods in longitudinal studies: a review ⋮ Likelihood based frequentist inference when data are missing at random ⋮ Valid statistical inference methods for incomplete contingency table with three-category missing data ⋮ The analysis of ordinal time-series data via a transition (Markov) model ⋮ Every Missingness not at Random Model Has a Missingness at Random Counterpart with Equal Fit ⋮ Bayesian varying coefficient mixed-effects joint models with asymmetry and missingness ⋮ Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout ⋮ Identification problem of transition models for repeated measurement data with nonignorable missing values ⋮ Functional approach of flexibly modelling generalized longitudinal data and survival time ⋮ The nature of sensitivity in monotone missing not at random models ⋮ Marginal and association regression models for longitudinal binary data with drop-outs: A likelihood-based approach ⋮ Marginalized transition shared random effects models for longitudinal binary data with nonignorable dropout ⋮ Indices for covariance mis-specification in longitudinal data analysis with no missing responses and with MAR drop-outs ⋮ An Appraisal of Methods for the Analysis of Longitudinal Ordinal Response Data with Random Dropout Using a Nonhomogeneous Markov Model ⋮ Bivariate Binary Data Analysis with Nonignorably Missing Outcomes ⋮ Influence analysis to assess sensitivity of the dropout process. ⋮ A pattern-mixture odds ratio model for incomplete categorical data ⋮ Nonignorable Models for Intermittently Missing Categorical Longitudinal Responses ⋮ A Latent‐Class Mixture Model for Incomplete Longitudinal Gaussian Data ⋮ Pattern-mixture models for categorical outcomes with non-monotone missingness ⋮ Methods for Conducting Sensitivity Analysis of Trials with Potentially Nonignorable Competing Causes of Censoring ⋮ A Pattern-Mixture Model for Longitudinal Binary Responses with Nonignorable Nonresponse ⋮ A Local Influence Approach Applied to Binary Data from a Psychiatric Study ⋮ A general joint model for longitudinal measurements and competing risks survival data with heterogeneous random effects ⋮ A model for overdispersed hierarchical ordinal data ⋮ A Hybrid Model for Nonignorable Dropout in Longitudinal Binary Responses ⋮ A Dirichlet process mixture model for non-ignorable dropout ⋮ Covariance miss-specification and the local influence approach in sensitivity analyses of longitudinal data with drop-outs ⋮ A Semiparametric Bayesian Approach to Dropout in Longitudinal Studies With Auxiliary Covariates ⋮ A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data ⋮ Models for the association between ordinal variables. ⋮ A semiparametric marginalized model for longitudinal data with informative dropout ⋮ Assessing uncertainty about parameter estimates with incomplete repeated ordinal data
This page was built for publication: The analysis of longitudinal ordinal data with nonrandom drop-out