R package rstpm2. Generalized Survival Models. R implementation of generalized survival models, where g(S(t|x))=eta(t,x) for a link function g, survival S at time t with covariates x and a linear predictor eta(t,x). The main assumption is that the time effect(s) are smooth. For fully parametric models, this re-implements Stata’s ’stpm2’ function, which are flexible parametric survival models developed by Royston and colleagues. We have extended the parametric models to include any smooth parametric smoothers for time. We have also extended the model to include any smooth penalized smoothers from the ’mgcv’ package, using penalized likelihood. These models include left truncation, right censoring, interval censoring, gamma frailties and normal random effects.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Benjamin Christoffersen: dynamichazard: Dynamic Hazard Models Using State Space Models (2021) not zbMATH
- Hadrien Charvat, Aurelien Belot: mexhaz: An R Package for Fitting Flexible Hazard-Based Regression Models for Overall and Excess Mortality with a Random Effect (2021) not zbMATH
- Samuel L. Brilleman, Rory Wolfe, Margarita Moreno-Betancur, Michael J. Crowther: Simulating Survival Data Using the simsurv R Package (2021) not zbMATH
- Soetewey, Antoine; Legrand, Catherine; Denuit, Michel; Silversmit, Geert: Waiting period from diagnosis for mortgage insurance issued to cancer survivors (2021)
- Mathieu Fauvernier; Laurent Remontet; Zoé Uhry; Nadine Bossard; Laurent Roche: survPen: an R package for hazard and excess hazard modelling with multidimensional penalized splines (2019) not zbMATH