In this paper the R package TP.idm to compute an empirical transition probability matrix for the illness-death model is introduced. This package implements a novel nonparametric estimator which is particularly well suited for non-Markov processes observed under right censoring. Variance estimates and confidence limits are also implemented in the package.Spanish Ministry of Economy and Competitiveness | Ref. MTM2014-55966-
One major goal in clinical applications of multi-state models is the estimation of transition probab...
Abstract One important goal in multi-state modeling is the estimation of transi-tion probabilities. ...
The Aalen-Johansen estimator for calculation of transition probabilities in a multi-state model, bui...
In this paper the R package TP.idm to compute an empirical transition probability matrix for the ill...
In this paper the R package TP.idm to compute an empirical transition probability matrix for the ill...
One major goal in clinical applications of multi-state models is the estimation of transition probab...
One major goal in clinical applications of multi-state models is the estimation of transitionprobabi...
Multi-state models can be successfully used for modelling complex event history data. In these model...
Multi-state models are often used for modeling complex event history data. In these models the estim...
One important goal in multi-state modeling is the estimation of transition probabilities. In longitu...
When dealing with complex event history data in which individuals may experience more than one singl...
Multi-state models can be successfully used for describing complicated event history data, for examp...
The ranked set sampling (RSS) design is applied widely in agriculture, environmental science, and me...
This paper describes the implementation of a flexible method in R for fitting a regression model to...
Multi-State models provide a relevant framework for modelling complex event histories. Quantities of...
One major goal in clinical applications of multi-state models is the estimation of transition probab...
Abstract One important goal in multi-state modeling is the estimation of transi-tion probabilities. ...
The Aalen-Johansen estimator for calculation of transition probabilities in a multi-state model, bui...
In this paper the R package TP.idm to compute an empirical transition probability matrix for the ill...
In this paper the R package TP.idm to compute an empirical transition probability matrix for the ill...
One major goal in clinical applications of multi-state models is the estimation of transition probab...
One major goal in clinical applications of multi-state models is the estimation of transitionprobabi...
Multi-state models can be successfully used for modelling complex event history data. In these model...
Multi-state models are often used for modeling complex event history data. In these models the estim...
One important goal in multi-state modeling is the estimation of transition probabilities. In longitu...
When dealing with complex event history data in which individuals may experience more than one singl...
Multi-state models can be successfully used for describing complicated event history data, for examp...
The ranked set sampling (RSS) design is applied widely in agriculture, environmental science, and me...
This paper describes the implementation of a flexible method in R for fitting a regression model to...
Multi-State models provide a relevant framework for modelling complex event histories. Quantities of...
One major goal in clinical applications of multi-state models is the estimation of transition probab...
Abstract One important goal in multi-state modeling is the estimation of transi-tion probabilities. ...
The Aalen-Johansen estimator for calculation of transition probabilities in a multi-state model, bui...