Nonignorable dropout models for longitudinal binary data with random effects: An application of Monte Carlo approximation through the Gibbs output

2009 ◽  
Vol 53 (12) ◽  
pp. 4530-4545 ◽  
Author(s):  
Jennifer S.K. Chan ◽  
Doris Y.P. Leung ◽  
S.T. Boris Choy ◽  
Wai Y. Wan
Keyword(s):  
Monte Carlo ◽  
Random Effects ◽  
Binary Data ◽  
Longitudinal Binary Data ◽  
Monte Carlo Approximation ◽  
Nonignorable Dropout

scholarly journals Modeling overdispersed longitudinal binary data using a combined beta and normal random-effects model

2012 ◽  
Vol 70 (1) ◽  
Author(s):  
Wondwosen Kassahun ◽  
Thomas Neyens ◽  
Geert Molenberghs ◽  
Christel Faes ◽  
Geert Verbeke
Keyword(s):  
Random Effects ◽  
Binary Data ◽  
Random Effects Model ◽  
Longitudinal Binary Data

Marginalized random effects models for multivariate longitudinal binary data

2009 ◽  
Vol 28 (8) ◽  
pp. 1284-1300 ◽  
Author(s):  
Keunbaik Lee ◽  
Yongsung Joo ◽  
Jae Keun Yoo ◽  
JungBok Lee
Keyword(s):  
Random Effects ◽  
Binary Data ◽  
Random Effects Models ◽  
Longitudinal Binary Data

Analysis of crossover designs for longitudinal binary data with ignorable and nonignorable dropout

2021 ◽  
pp. 096228022110471
Author(s):  
Xi Wang ◽  
Vernon M. Chinchilli
Keyword(s):  
Sensitivity Analysis ◽  
Missing Data ◽  
Binary Data ◽  
Likelihood Estimation ◽  
Real Data ◽  
Primary Objective ◽  
Crossover Designs ◽  
Longitudinal Binary Data ◽  
Pseudo Likelihood Estimation ◽  
Nonignorable Dropout

Longitudinal binary data in crossover designs with missing data due to ignorable and nonignorable dropout is common. This paper evaluates available conditional and marginal models and establishes the relationship between the conditional and marginal parameters with the primary objective of comparing the treatment mean effects. We perform extensive simulation studies to investigate these models under complete data and the selection models under missing data with different parametric distributions and missingness patterns and mechanisms. The generalized estimating equations and the generalized linear mixed-effects models with pseudo-likelihood estimation are advocated for valid and robust inference. We also propose a controlled multiple imputation method as a sensitivity analysis of the missing data assumption. Lastly, we implement the proposed models and the sensitivity analysis in two real data examples with binary data.

Two‐wave two‐phase outcome‐dependent sampling designs, with applications to longitudinal binary data

2021 ◽  
Author(s):  
Ran Tao ◽  
Nathaniel D. Mercaldo ◽  
Sebastien Haneuse ◽  
Jacob M. Maronge ◽  
Paul J. Rathouz ◽  
...  
Keyword(s):  
Binary Data ◽  
Two Phase ◽  
Longitudinal Binary Data

scholarly journals Branching diffusion representation of semilinear PDEs and Monte Carlo approximation

2019 ◽  
Vol 55 (1) ◽  
pp. 184-210 ◽  
Author(s):  
Pierre Henry-Labordère ◽  
Nadia Oudjane ◽  
Xiaolu Tan ◽  
Nizar Touzi ◽  
Xavier Warin
Keyword(s):  
Monte Carlo ◽  
Branching Diffusion ◽  
Monte Carlo Approximation ◽  
Semilinear Pdes

A Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros

Biometrics ◽  
2008 ◽  
Vol 64 (2) ◽  
pp. 611-619 ◽  
Author(s):  
Dimitris Rizopoulos ◽  
Geert Verbeke ◽  
Emmanuel Lesaffre ◽  
Yves Vanrenterghem
Keyword(s):  
Binary Data ◽  
Joint Model ◽  
Excess Zeros ◽  
Longitudinal Binary Data
Sign in / Sign up

Export Citation Format

Share Document