Testing the proportional odds assumption in multiply imputed ordinal longitudinal data

DOI10.1080/02664763.2015.1023704OpenAlexW1978590959MaRDI QIDQ5130336

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Publication date: 4 November 2020

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2015.1023704

zbMATH Keywords

clinical trialsmissing datacategorical outcomemultivariate normal imputationlongitudinal analysisproportional odds assumptionordinal imputation model

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Bias Reduction in Logistic Regression with Missing Responses When the Missing Data Mechanism is Nonignorable

Uses Software

• R
• SAS

Cites Work

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• Longitudinal data analysis using generalized linear models
• Some simple methods for generating correlated categorical variates
• On the existence of maximum likelihood estimates in logistic regression models
• Hypothesis testing of regression parameters in semiparametric generalized linear models for cluster correlated data
• Partial Proportional Odds Models for Ordinal Response Variables
• The Calculation of Posterior Distributions by Data Augmentation
• A Tutorial on the SWEEP Operator
• Inference and missing data
• Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
• Semiparametric Efficiency in Multivariate Regression Models with Missing Data
• Modeling the Drop-Out Mechanism in Repeated-Measures Studies
• A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data
• Multiple imputation of discrete and continuous data by fully conditional specification
• Estimation of the probability of an event as a function of several independent variables

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