rstpm2

Smooth Survival Models, Including Generalized Survival Models

R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, 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 <doi:10.1177/0962280216664760>. For fully parametric models with natural splines, 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 <doi:10.1002/sim.7451>. For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.

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Description file content

Package
rstpm2
Type
Package
Title
Smooth Survival Models, Including Generalized Survival Models
Version
1.5.1
Date
2019-10-28
Depends
R (>= 3.0.2), methods, survival, splines
Imports
graphics, Rcpp (>= 0.10.2), stats, mgcv, bbmle (>= 1.0.20), fastGHQuad, deSolve, utils, parallel
Suggests
eha, testthat, ggplot2, lattice, readstata13, mstate
LinkingTo
Rcpp,RcppArmadillo
Maintainer
Mark Clements
Description
R implementation of generalized survival models (GSMs), smooth accelerated failure time (AFT) models and Markov multi-state models. For the GSMs, 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 with natural splines, 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 . For the smooth AFTs, S(t|x) = S_0(t*eta(t,x)), where the baseline survival function S_0(t)=exp(-exp(eta_0(t))) is modelled for natural splines for eta_0, and the time-dependent cumulative acceleration factor eta(t,x)=\int_0^t exp(eta_1(u,x)) du for log acceleration factor eta_1(u,x). The Markov multi-state models allow for a range of models with smooth transitions to predict transition probabilities, length of stay, utilities and costs, with differences, ratios and standardisation.
URL
BugReports
http://github.com/mclements/rstpm2/issues
License
GPL-2 | GPL-3
LazyData
yes
NeedsCompilation
yes
Packaged
2019-11-05 07:48:56 UTC; marcle
Author
Mark Clements [aut, cre], Xing-Rong Liu [aut], Paul Lambert [ctb], Lasse Hjort Jakobsen [ctb], Alessandro Gasparini [ctb], Gordon Smyth [cph], Patrick Alken [cph], Simon Wood [cph], Rhys Ulerich [cph]
Repository
CRAN
Date/Publication
2019-11-05 23:00:05 UTC

install.packages('rstpm2')

1.5.1

10 days ago

http://github.com/mclements/rstpm2

Mark Clements

GPL-2 | GPL-3

Depends on

R (>= 3.0.2), methods, survival, splines

Imports

graphics, Rcpp (>= 0.10.2), stats, mgcv, bbmle (>= 1.0.20), fastGHQuad, deSolve, utils, parallel

Suggests

eha, testthat, ggplot2, lattice, readstata13, mstate

Discussions