flexsurv was written to encourage the use of flexible
distributions to account for model uncertainty in survival analysis,
initially the three-parameter generalized gamma, four-parameter
generalized F and the Royston-Parmar spline
model. However it was straightforward to modularise the design of the
code to accept any generic parametric distribution. survreg from the survival package, the
recommended R package for survival analysis, supports two-parameter
location-scale parametric models. The eha package includes functions phreg and
aftreg for parametric survival modelling under a
variety of distributions and proportional hazards or accelerated
failure time parameterisations. Other facilities for generic maximum likelihood model fitting exist,
for example fitdistr in the MASS package.
flexsurvreg is intended to provide typical outputs and
summaries of interest to survival analysts, particularly in medical
applications. Feature requests along these lines are welcome. Note that if an R package provides density and probability functions
for a parametric distribution, it can then be used easily in
flexsurvreg. For instance, several ``reliability''
distributions used in industrial statistics are available in the
VGAM and package, and many other survival distributions
are provided in ActuDistns.
Please report unexplained inconsistencies in
results between flexsurv and other software.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.
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