scholarly journals Likelihood and pseudo-likelihood methods for semiparametric joint models for a primary endpoint and longitudinal data

2007 ◽  
Vol 51 (12) ◽  
pp. 5776-5790 ◽  
Author(s):  
Erning Li ◽  
Daowen Zhang ◽  
Marie Davidian
Keyword(s):  
Longitudinal Data ◽  
Primary Endpoint ◽  
Joint Models ◽  
Likelihood Methods ◽  
Pseudo Likelihood

scholarly journals Mixed-Effects Tobit Joint Models for Longitudinal Data with Skewness, Detection Limits, and Measurement Errors

2012 ◽  
Vol 2012 ◽  
pp. 1-19 ◽  
Author(s):  
Getachew A. Dagne ◽  
Yangxin Huang
Keyword(s):  
Detection Limit ◽  
Longitudinal Data ◽  
Normal Distribution ◽  
Measurement Errors ◽  
Real Data ◽  
Mixed Effects ◽  
Parameter Estimates ◽  
Joint Models ◽  
Departure From Normality ◽  
Invalid Inference

Complex longitudinal data are commonly analyzed using nonlinear mixed-effects (NLME) models with a normal distribution. However, a departure from normality may lead to invalid inference and unreasonable parameter estimates. Some covariates may be measured with substantial errors, and the response observations may also be subjected to left-censoring due to a detection limit. Inferential procedures can be complicated dramatically when such data with asymmetric characteristics, left censoring, and measurement errors are analyzed. There is relatively little work concerning all of the three features simultaneously. In this paper, we jointly investigate a skew-tNLME Tobit model for response (with left censoring) process and a skew-tnonparametric mixed-effects model for covariate (with measurement errors) process under a Bayesian framework. A real data example is used to illustrate the proposed methods.

Pseudo-likelihood methods for longitudinal binary data with non-ignorable missing responses and covariates

2006 ◽  
Vol 25 (16) ◽  
pp. 2784-2796 ◽  
Author(s):  
Michael Parzen ◽  
Stuart R. Lipsitz ◽  
Garrett M. Fitzmaurice ◽  
Joseph G. Ibrahim ◽  
Andrea Troxel
Keyword(s):  
Binary Data ◽  
Likelihood Methods ◽  
Missing Responses ◽  
Pseudo Likelihood ◽  
Longitudinal Binary Data

scholarly journals Pseudo-likelihood methods for community detection in large sparse networks

2013 ◽  
Vol 41 (4) ◽  
pp. 2097-2122 ◽  
Author(s):  
Arash A. Amini ◽  
Aiyou Chen ◽  
Peter J. Bickel ◽  
Elizaveta Levina
Keyword(s):  
Community Detection ◽  
Likelihood Methods ◽  
Pseudo Likelihood ◽  
Sparse Networks

scholarly journals Joint Models of Longitudinal Outcomes and Informative Time

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
JangDong Seo
Keyword(s):  
Longitudinal Data ◽  
Real Data ◽  
Parameter Estimates ◽  
Data Sets ◽  
Joint Models ◽  
Time Intervals ◽  
Asymptotic Behaviors ◽  
Longitudinal Outcomes ◽  
Data Analyses

Longitudinal data analyses commonly assume that time intervals are predetermined and have no information regarding the outcomes. However, there might be irregular time intervals and informative time. Presented are joint models and asymptotic behaviors of the parameter estimates. Also, the models are applied for real data sets.

scholarly journals Joint Models of Longitudinal Data and Recurrent Events with Informative Terminal Event

2012 ◽  
Vol 4 (2) ◽  
pp. 262-281 ◽  
Author(s):  
Sehee Kim ◽  
Donglin Zeng ◽  
Lloyd Chambless ◽  
Yi Li
Keyword(s):  
Longitudinal Data ◽  
Recurrent Events ◽  
Terminal Event ◽  
Joint Models
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