flexsurv-package | R Documentation |

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.

`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`

.

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.

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

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

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