A fast EM algorithm for fitting joint models of a binary response and multiple longitudinal covariates subject to detection limits
DOI10.1016/j.csda.2014.11.011OpenAlexW1991403142WikidataQ34970198 ScholiaQ34970198MaRDI QIDQ62047
Daowen Zhang, Huixia Judy Wang, Paul W. Bernhardt, Daowen Zhang, Paul W. Bernhardt, Huixia Judy Wang
Publication date: May 2015
Published in: Computational Statistics & Data Analysis, Computational Statistics and Data Analysis (Search for Journal in Brave)
Full work available at URL: http://europepmc.org/articles/pmc4295570
EM algorithmdetection limitlogistic regressionnormal approximationjoint modelmultiple longitudinal covariates
Computational methods for problems pertaining to statistics (62-08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Generalized linear models (logistic models) (62J12) Estimation in survival analysis and censored data (62N02)
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- Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule
- Asymptotic normality of posterior distributions for generalized linear mixed models
- The joint model of the logistic model and linear random effect model -- an application to predict orthostatic hypertension for subacute stroke patients
- A penalized likelihood approach to joint modeling of longitudinal measurements and time-to-event data
- Joint modelling of multivariate longitudinal outcomes and a time-to-event: a nonlinear latent class approach
- Likelihood and pseudo-likelihood methods for semiparametric joint models for a primary endpoint and longitudinal data
- On the convergence properties of the EM algorithm
- Flexible modeling of survival data with covariates subject to detection limits via multiple imputation
- Joint Models for Longitudinal and Time-to-Event Data
- Mixed model analysis of censored longitudinal data with flexible random-effects density
- Fully Exponential Laplace Approximations for the Joint Modelling of Survival and Longitudinal Data
- Censored observations, repeated measures and mixed effects models: An approach using the EM algorithm and normal errors
- Mixed-Model Analysis of a Censored Normal Distribution with Reference to Animal Breeding
- Bias in 2-part mixed models for longitudinal semicontinuous data
- Conditional Estimation for Generalized Linear Models When Covariates Are Subject‐Specific Parameters in a Mixed Model for Longitudinal Measurements
- Estimation and Comparison of Changes in the Presence of Informative Right Censoring by Modeling the Censoring Process
- Joint inference for nonlinear mixed-effects models and time to event at the presence of missing data
- Mixed‐Effect Hybrid Models for Longitudinal Data with Nonignorable Dropout
- A Joint Model for Survival and Longitudinal Data Measured with Error
- A Joint Model for Nonlinear Mixed-Effects Models With Censoring and Covariates Measured With Error, With Application to AIDS Studies
- Random Regression Models for Human Immunodeficiency Virus Ribonucleic Acid Data Subject to Left Censoring and Informative Drop-Outs
- Mixed Effects Logistic Regression Models for Longitudinal Binary Response Data with Informative Drop-Out
- Regression Analysis When Covariates Are Regression Parameters of a Random Effects Model for Observed Longitudinal Measurements
- Mixed Effects Logistic Regression Models for Longitudinal Ordinal Functional Response Data with Multiple‐Cause Drop‐Out from the Longitudinal Study of Aging
- Joint modeling of censored longitudinal and event time data
- Joint Models for a Primary Endpoint and Multiple Longitudinal Covariate Processes
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