We propose a straightforward approach for simulation of discrete random variables with overdispersion, specified marginal means, and product correlations that are plausible for longitudinal data with equal, or unequal, temporal spacings. The method stems from results we prove for variables with first-order antedependence and linearity of the conditional expectations. The proposed approach will be especially useful for assessment of methods such as generalized estimating equations, which specify separate models for the marginal means and correlation structure of measurements on a subject
We consider solutions to generalized estimating equations with singular working correlation matrices...
The focus of this research is to improve existing methods for the marginal modeling of associated ca...
Marginalised models, also known as marginally specified models, have recently become a popular tool ...
Medical researchers strive to collect complete information, but most studies will have some degree o...
This manuscript implements a maximum likelihood based approach that is appropriate for equally spac...
Medical researchers strive to collect complete information, but most studies will have some degree o...
Overdispersion and correlation are two features often encountered when modeling non-Gaussian depende...
Correlated multivariate Poisson and binary variables occur naturally in medical, biological and epid...
In this paper, a new discrete statistical model for ordered categorical data is proposed via fixed-p...
The ability to simulate correlated binary data is important for sample size calculation and comparis...
Observational longitudinal data on treatments and covariates are increasingly used to investigate tr...
Abstract: Observational longitudinal data on treatments and covariates are increasingly used to inve...
We develop a new approach to using estimating equations to estimate marginal regression models for l...
Consider estimation of causal parameters in a marginal structural model for the discrete intensity o...
Obra ressenyada: Dale ZIMMERMAN and Vicente NÚÑEZ ANTÓN, Antedependence Models for Longitudinal Data...
We consider solutions to generalized estimating equations with singular working correlation matrices...
The focus of this research is to improve existing methods for the marginal modeling of associated ca...
Marginalised models, also known as marginally specified models, have recently become a popular tool ...
Medical researchers strive to collect complete information, but most studies will have some degree o...
This manuscript implements a maximum likelihood based approach that is appropriate for equally spac...
Medical researchers strive to collect complete information, but most studies will have some degree o...
Overdispersion and correlation are two features often encountered when modeling non-Gaussian depende...
Correlated multivariate Poisson and binary variables occur naturally in medical, biological and epid...
In this paper, a new discrete statistical model for ordered categorical data is proposed via fixed-p...
The ability to simulate correlated binary data is important for sample size calculation and comparis...
Observational longitudinal data on treatments and covariates are increasingly used to investigate tr...
Abstract: Observational longitudinal data on treatments and covariates are increasingly used to inve...
We develop a new approach to using estimating equations to estimate marginal regression models for l...
Consider estimation of causal parameters in a marginal structural model for the discrete intensity o...
Obra ressenyada: Dale ZIMMERMAN and Vicente NÚÑEZ ANTÓN, Antedependence Models for Longitudinal Data...
We consider solutions to generalized estimating equations with singular working correlation matrices...
The focus of this research is to improve existing methods for the marginal modeling of associated ca...
Marginalised models, also known as marginally specified models, have recently become a popular tool ...