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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")

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Install

install.packages('rstpm2')

Monthly Downloads

7,371

Version

1.3.2

License

GPL-2 | GPL-3

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Maintainer

Mark Clements

Last Published

April 13th, 2016

Functions in rstpm2 (1.3.2)

incrVar

Utility that returns a function to increment a variable in a data-frame.
plot-methods

plots for an stpm2 fit
pstpm2-class

Class "pstpm2"
Rstpm2-package

Flexible parametric survival models.
predictnl

Generic function for non-linear prediction.
grad

gradient function (internal function)
predict-methods

Predicted values for an stpm2 or pstpm2 fit
colon

Colon cancer.
popmort

Background mortality rates for the colon dataset.
stpm2

Fully parametric generalised survival model
numDeltaMethod

Calculate numerical delta method for non-linear predictions.
legendre.quadrature.rule.200

Legendre quadrature rule for n=200.
predict.nsx

Evaluate a Spline Basis
stpm2-class

Class "stpm2" ~~~
nsx

Generate a Basis Matrix for Natural Cubic Splines (with eXtensions)
predictnl-methods

~~ Methods for Function predictnl ~~
rstpm2-internal

Internal functions for the rstpm2 package.
coef<-

Generic method to update the coef in an object.
brcancer

German breast cancer data from Stata.
pstpm2

Penalised generalised survival model
predictnl.default

Default implementation of the predictnl generic function.