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>, and copulas. 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.

Version: 1.6.5
Depends: R (≥ 3.0.2), methods, survival, splines
Imports: graphics, Rcpp (≥ 0.10.2), stats, mgcv, bbmle (≥ 1.0.20), fastGHQuad, utils, parallel, mvtnorm
LinkingTo: Rcpp, RcppArmadillo, BH
Suggests: eha, testthat, ggplot2, lattice, readstata13, mstate, scales, survPen, flexsurv, timereg, deSolve
Published: 2024-08-20
DOI: 10.32614/CRAN.package.rstpm2
Author: Mark Clements [aut, cre], Xing-Rong Liu [aut], Benjamin Christoffersen [aut], Paul Lambert [ctb], Lasse Hjort Jakobsen [ctb], Alessandro Gasparini [ctb], Gordon Smyth [cph], Patrick Alken [cph], Simon Wood [cph], Rhys Ulerich [cph]
Maintainer: Mark Clements <mark.clements at ki.se>
BugReports: https://github.com/mclements/rstpm2/issues
License: GPL-2 | GPL-3
URL: https://github.com/mclements/rstpm2
NeedsCompilation: yes
Citation: rstpm2 citation info
Materials: README NEWS
In views: Survival
CRAN checks: rstpm2 results

Documentation:

Reference manual: rstpm2.pdf
Vignettes: Introduction to the rstpm2 Package (source, R code)
\texttt{rstpm2}: a simple guide (source, R code)
Predictions for Markov multi-state models (source)
Introduction to the predictnl function (source, R code)

Downloads:

Package source: rstpm2_1.6.5.tar.gz
Windows binaries: r-devel: rstpm2_1.6.5.zip, r-release: rstpm2_1.6.5.zip, r-oldrel: rstpm2_1.6.5.zip
macOS binaries: r-release (arm64): rstpm2_1.6.5.tgz, r-oldrel (arm64): rstpm2_1.6.5.tgz, r-release (x86_64): rstpm2_1.6.5.tgz, r-oldrel (x86_64): rstpm2_1.6.5.tgz
Old sources: rstpm2 archive

Reverse dependencies:

Reverse depends: cuRe
Reverse imports: afthd, eventPred, EventPredInCure, flexsurv, JointFPM, TukeyGH77
Reverse suggests: biostat3, marginaleffects, mexhaz, multinma, rsimsum, simsurv

Linking:

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