mstate

R package mstate: Data preparation, estimation and prediction in multi-state models , Functions for data preparation, descriptives, hazard estimation and prediction with Aalen-Johansen or simulation in competing risks and multi-state models (Source: http://cran.r-project.org/web/packages)

Keywords for this software

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References in zbMATH (referenced in 32 articles , 1 standard article )

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  1. Rui J. Costa, Moritz Gerstung: The R package ebmstate for disease progression analysis under empirical Bayes Cox models (2022) arXiv
  2. Maltzahn, Niklas; Hoff, Rune; Aalen, Odd O.; Mehlum, Ingrid S.; Putter, Hein; Gran, Jon Michael: A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models (2021)
  3. Saha, Sudipta; Liu, Zhihui; Saarela, Olli: Instrumental variable estimation of early treatment effect in randomized screening trials (2021)
  4. Chowdhury, Rafiqul I.; Islam, M. Ataharul: Prediction of risks of sequence of events using multistage proportional hazards model: a marginal-conditional modelling approach (2020)
  5. Hoff, Rune; Putter, Hein; Mehlum, Ingrid Sivesind; Gran, Jon Michael: Landmark estimation of transition probabilities in non-Markov multi-state models with covariates (2019)
  6. 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
  7. Guibert, Quentin; Planchet, Frédéric: Non-parametric inference of transition probabilities based on Aalen-Johansen integral estimators for acyclic multi-state models: application to LTC insurance (2018)
  8. Sharples, Linda D.: The role of statistics in the era of big data: electronic health records for healthcare research (2018)
  9. Vanesa Balboa; Jacobo de Uña-Álvarez: Estimation of Transition Probabilities for the Illness-Death Model: Package TP.idm (2018) not zbMATH
  10. 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
  11. Moriña, D.; Navarro, A.: Competing risks simulation with the survsim R package (2017)
  12. Christopher Jackson: flexsurv: A Platform for Parametric Survival Modeling in R (2016) not zbMATH
  13. Commenges, Daniel; Jacqmin-Gadda, Hélène: Dynamical biostatistical models (2016)
  14. Delord, Marc; Génin, Emmanuelle: Multiple imputation for competing risks regression with interval-censored data (2016)
  15. Moore, Dirk F.: Applied survival analysis using R (2016)
  16. Reulen, Holger; Kneib, Thomas: Boosting multi-state models (2016)
  17. Teixeira, Laetitia; Cadarso-Suárez, Carmen; Rodrigues, Anabela; Mendonça, Denisa: Time-dependent ROC methodology to evaluate the predictive accuracy of semiparametric multi-state models in the presence of competing risks: an application to peritoneal dialysis programme (2016)
  18. Agnieszka Król; Philippe Saint-Pierre: SemiMarkov: An R Package for Parametric Estimation in Multi-State Semi-Markov Models (2015) not zbMATH
  19. de Castro, Mário; Chen, Ming-Hui; Zhang, Yuanye: Bayesian path specific frailty models for multi-state survival data with applications (2015)
  20. Martínez-Camblor, Pablo; de Uña-Álvarez, Jacobo; Díaz Corte, Carmen: Expanded renal transplantation: a competing risk model approach (2015)

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