Joint modeling of covariates and censoring process assuming non-constant dropout hazard
DOI10.1007/S10260-015-0302-2zbMath1372.62041OpenAlexW2019567395WikidataQ37231183 ScholiaQ37231183MaRDI QIDQ2013641
Publication date: 8 August 2017
Published in: Statistical Methods and Applications (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc5010875
maximum likelihood estimationlongitudinal datarandom effectscensoringbootstrappinginformative dropoutdiscrete survival modelnon-constant hazardtransplant patients
Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Generalized linear models (logistic models) (62J12)
Uses Software
Cites Work
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- Linear Mixed Models with Flexible Distributions of Random Effects for Longitudinal Data
- Robustness of the linear mixed model to misspecified error distribution
- Estimation and comparison of Changes in the Presence of Informative Right Censoring: Conditional Linear Model
- Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
- Regression Analysis When Covariates Are Regression Parameters of a Random Effects Model for Observed Longitudinal Measurements
- Slope Estimation in the Presence of Informative Right Censoring: Modeling the Number of Observations as a Geometric Random Variable
- An Approximate Generalized Linear Model with Random Effects for Informative Missing Data
- Slope estimation of covariates that influence renal outcome following renal transplant adjusting for informative right censoring
- Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles
- Calculation of Gauss Quadrature Rules
- Prediction of pregnancy: a joint model for longitudinal and binary data
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