Learn R Programming

⚠️There's a newer version (1.6.7) of this package.Take me there.

rstpm2: An R package for link-based survival models

Introduction

This package provides link-based survival models that extend the Royston-Parmar models, a family of flexible parametric models. There are two main classes included in this package:

A. The class stpm2 is an R version of stpm2 in Stata with some extensions, including:

  1. Multiple links (log-log, -probit, -logit);

  2. Left truncation and right censoring (with experimental support for interval censoring);

  3. Relative survival;

  4. Cure models (where we introduce the nsx smoother, which extends the ns smoother);

  5. Predictions for survival, hazards, survival differences, hazard differences, mean survival, etc;

  6. Functional forms can be represented in regression splines or other parametric forms;

  7. The smoothers for time can use any transformation of time, including no transformation or log(time).

B. Another class pstpm2 is the implementation of the penalised models and corresponding penalized likelihood estimation methods. The main aim is to represent another way to deal with non-proportional hazards and adjust for potential continuous confounders in functional forms, not limited to proportional hazards and linear effect forms for all covariates. Functional forms can be represented in penalized regression splines (all mgcv smoothers ) or other parametric forms.

Some examples

The default for the parametric model is to use the Royston Parmar model, which uses a natural spline for the transformed baseline for log(time) with a log-log link.

require(rstpm2)
data(brcancer)
fit <- stpm2(Surv(rectime,censrec==1)~hormon,data=brcancer,df=3)
plot(fit,newdata=data.frame(hormon=0),type="hazard")

The default for the penalised model is similar, using a thin-plate spline for the transformed baseline for log(time) with a log-log link. The advantage of the penalised model is that there is no need to specify the knots or degrees of freedom for the baseline smoother.

fit <- pstpm2(Surv(rectime,censrec==1)~hormon,data=brcancer)
plot(fit,newdata=data.frame(hormon=0),type="hazard")

Copy Link

Version

Install

install.packages('rstpm2')

Monthly Downloads

9,181

Version

1.4.1

License

GPL-2 | GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Mark Clements

Last Published

September 20th, 2017

Functions in rstpm2 (1.4.1)

pstpm2-class

Class "pstpm2"
rstpm2-internal

Internal functions for the rstpm2 package.
nsxD

Generate a Basis Matrix for the first derivative of Natural Cubic Splines (with eXtensions)
tvcCoxph-class

Class "tvcCoxph"
stpm2-class

Class "stpm2" ~~~
stpm2

Fully parametric generalised survival model
residuals-methods

Residual values for an stpm2 or pstpm2 fit
pstpm2

Penalised generalised survival model
incrVar

Utility that returns a function to increment a variable in a data-frame.
Rstpm2-package

Flexible parametric survival models.
legendre.quadrature.rule.200

Legendre quadrature rule for n=200.
grad

gradient function (internal function)
coef<-

Generic method to update the coef in an object.
brcancer

German breast cancer data from Stata.
aft

Parametric accelerated failure time model with smooth time functions
aft-class

Class "stpm2" ~~~
colon

Colon cancer.
cox.tvc

Test for a time-varying effect in the coxph model
predictnl.default

Default implementation of the predictnl generic function.
plot-methods

plots for an stpm2 fit
predict.nsx

Evaluate a Spline Basis
predict-methods

Predicted values for an stpm2 or pstpm2 fit
predictnl

Generic function for non-linear prediction.
numDeltaMethod

Calculate numerical delta method for non-linear predictions.
nsx

Generate a Basis Matrix for Natural Cubic Splines (with eXtensions)
popmort

Background mortality rates for the colon dataset.
predictnl-methods

~~ Methods for Function predictnl ~~