TPmsm: Estimation of the Transition Probabilities in 3-State Models. One major goal in clinical applications of multi-state models is the estimation of transition probabilities. The usual nonparametric estimator of the transition matrix for non-homogeneous Markov processes is the Aalen-Johansen estimator (Aalen and Johansen 1978). However, two problems may arise from using this estimator: first, its standard error may be large in heavy censored scenarios; second, the estimator may be inconsistent if the process is non-Markovian. The development of the R package TPmsm has been motivated by several recent contributions that account for these estimation problems. Estimation and statistical inference for transition probabilities can be performed using TPmsm. The TPmsm package provides seven different approaches to three-state illness-death modeling. In two of these approaches the transition probabilities are estimated conditionally on current or past covariate measures. Two real data examples are included for illustration of software usage.
Keywords for this software
References in zbMATH (referenced in 4 articles , 1 standard article )
Showing results 1 to 4 of 4.
- Danilo Alvares, Sebastien Haneuse, Catherine Lee, Kyu Ha Lee: SemiCompRisks: An R Package for Independent and Cluster-Correlated Analyses of Semi-Competing Risks Data (2018) arXiv
- Vanesa Balboa; Jacobo de Uña-Álvarez: Estimation of Transition Probabilities for the Illness-Death Model: Package TP.idm (2018) not zbMATH
- Célia Touraine and Thomas Gerds and Pierre Joly: SmoothHazard: An R Package for Fitting Regression Models to Interval-Censored Observations of Illness-Death Models (2017) not zbMATH
- Artur Araújo; Luís Meira-Machado; Javier Roca-Pardiñas: TPmsm: Estimation of the Transition Probabilities in 3-State Models (2014) not zbMATH