Multiple imputation for ordinal longitudinal data with monotone missing data patterns
From MaRDI portal
Publication:5138532
DOI10.1080/02664763.2016.1168370OpenAlexW2345307821MaRDI QIDQ5138532
A. Y. Kombo, Geert Molenberghs, Henry G. Mwambi
Publication date: 4 December 2020
Published in: Journal of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://lirias.kuleuven.be/handle/123456789/592116
longitudinal dataproportional odds modelmultivariate normal imputationfully conditional specificationordinal outcomemonotone missing data patterns
Related Items
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Longitudinal data analysis using generalized linear models
- A method for increasing the robustness of multiple imputation
- The analysis of ordered categorical data: An overview and a survey of recent developments. (With discussion)
- Models for discrete longitudinal data.
- Fully conditional specification in multivariate imputation
- Every Missingness not at Random Model Has a Missingness at Random Counterpart with Equal Fit
- The Calculation of Posterior Distributions by Data Augmentation
- Inference and missing data
- Formalizing Subjective Notions About the Effect of Nonrespondents in Sample Surveys
- Multiple Imputation and its Application
- Assessing Response Profiles from Incomplete Longitudinal Clinical Trial Data Under Regulatory Considerations
- Analysis of Semiparametric Regression Models for Repeated Outcomes in the Presence of Missing Data
- Informative Drop-Out in Longitudinal Data Analysis
- Multiple imputation: current perspectives
- Multiple imputation of discrete and continuous data by fully conditional specification
- Evaluation of software for multiple imputation of semi-continuous data
- Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: a simulation assessment