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rstpm2: An R package for link-based survival models

NOTE: versions 1.4.1 and 1.4.2 of rstpm2 included a critical bug in the predict function for type in "hr", "sdiff", "hdiff", "meansurvdiff", "meanhr", "or", "marghr" or "uncured".

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

1.5.1

License

GPL-2 | GPL-3

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Maintainer

Mark Clements

Last Published

November 5th, 2019

Functions in rstpm2 (1.5.1)

coef<-

Generic method to update the coef in an object.
cox.tvc

Test for a time-varying effect in the coxph model
eform.stpm2

S3 method for to provide exponentiated coefficents with confidence intervals.
stpm2-class

Class "stpm2" ~~~
markov_msm

Predictions for continuous time, nonhomogeneous Markov multi-state models using parametric and penalised survival models.
lines.stpm2

S3 methods for lines
tvcCoxph-class

Class "tvcCoxph"
brcancer

German breast cancer data from Stata.
colon

Colon cancer.
aft

Parametric accelerated failure time model with smooth time functions
incrVar

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

Estimation of standard errors using the numerical delta method.
legendre.quadrature.rule.200

Legendre quadrature rule for n=200.
grad

gradient function (internal function)
pstpm2-class

Class "pstpm2"
aft-class

Class "stpm2" ~~~
numDeltaMethod

Calculate numerical delta method for non-linear predictions.
Rstpm2-package

Flexible parametric survival models.
plot-methods

plots for an stpm2 fit
rstpm2-internal

Internal functions for the rstpm2 package.
nsx

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

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

Residual values for an stpm2 or pstpm2 fit
vuniroot

Vectorised One Dimensional Root (Zero) Finding
gsm

Parametric and penalised generalised survival models
popmort

Background mortality rates for the colon dataset.
predict.nsx

Evaluate a Spline Basis
predict-methods

Predicted values for an stpm2 or pstpm2 fit
predictnl-methods

~~ Methods for Function predictnl ~~