R: flexsurv: Flexible parametric survival and multi-state models

flexsurv-package {flexsurv} | R Documentation

## flexsurv: Flexible parametric survival and multi-state models

### Description

flexsurv: Flexible parametric models for time-to-event data, including the generalized gamma, the generalized F and the Royston-Parmar spline model, and extensible to user-defined distributions.

### Details

`flexsurvreg` fits parametric models for time-to-event (survival) data. Data may be right-censored, and/or left-censored, and/or left-truncated. Several built-in parametric distributions are available. Any user-defined parametric model can also be employed by supplying a list with basic information about the distribution, including the density or hazard and ideally also the cumulative distribution or hazard.

Covariates can be included using a linear model on any parameter of the distribution, log-transformed to the real line if necessary. This typically defines an accelerated failure time or proportional hazards model, depending on the distribution and parameter.

`flexsurvspline` fits the flexible survival model of Royston and Parmar (2002) in which the log cumulative hazard is modelled as a natural cubic spline function of log time. Covariates can be included on any of the spline parameters, giving either a proportional hazards model or an arbitrarily-flexible time-dependent effect. Alternative proportional odds or probit parameterisations are available.

Output from the models can be presented as survivor, cumulative hazard and hazard functions (`summary.flexsurvreg`). These can be plotted against nonparametric estimates (`plot.flexsurvreg`) to assess goodness-of-fit. Any other user-defined function of the parameters may be summarised in the same way.

Multi-state models for time-to-event data can also be fitted with the same functions. Predictions from those models can then be made using the functions `pmatrix.fs`, `pmatrix.simfs`, `totlos.fs`, `totlos.simfs`, or `sim.fmsm`, or alternatively by `msfit.flexsurvreg` followed by `mssample` or `probtrans` from the package mstate.

Distribution (“dpqr”) functions for the generalized gamma and F distributions are given in `GenGamma`, `GenF` (preferred parameterisations) and `GenGamma.orig`, `GenF.orig` (original parameterisations). `flexsurv` also includes the standard Gompertz distribution with unrestricted shape parameter, see `Gompertz`.

### User guide

The flexsurv user guide vignette explains the methods in detail, and gives several worked examples. A further vignette flexsurv-examples gives a few more complicated examples, and users are encouraged to submit their own.

### Author(s)

Christopher Jackson chris.jackson@mrc-bsu.cam.ac.uk

### References

Jackson, C. (2016). flexsurv: A Platform for Parametric Survival Modeling in R. Journal of Statistical Software, 70(8), 1-33. doi:10.18637/jss.v070.i08

Royston, P. and Parmar, M. (2002). Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects. Statistics in Medicine 21(1):2175-2197.

Cox, C. (2008). The generalized F distribution: An umbrella for parametric survival analysis. Statistics in Medicine 27:4301-4312.

Cox, C., Chu, H., Schneider, M. F. and Muñoz, A. (2007). Parametric survival analysis and taxonomy of hazard functions for the generalized gamma distribution. Statistics in Medicine 26:4252-4374

### See Also

Useful links:

- https://github.com/chjackson/flexsurv-dev

- Report bugs at https://github.com/chjackson/flexsurv-dev/issues

[Package flexsurv version 2.1 Index]