rmst_generic: Generic function to find restricted mean survival time for...

In chjackson/flexsurv-dev: Flexible Parametric Survival and Multi-State Models

Generic function to find restricted mean survival time for some distribution

Description

Generic function to find the restricted mean of a distribution, given the equivalent probability distribution function, using numeric integration.

Usage

rmst_generic(pdist, t, start = 0, matargs = NULL, scalarargs = NULL, ...)

Arguments

pdist	Probability distribution function, for example, pnorm for the normal distribution, which must be defined in the current workspace. This should accept and return vectorised parameters and values. It should also return the correct values for the entire real line, for example a positive distribution should have pdist(x)==0 for x<0.
t	Vector of times at which rmst is evaluated
start	Optional left-truncation time or times. The returned restricted mean survival will be conditioned on survival up to this time.
matargs	Character vector giving the elements of ... which represent vector parameters of the distribution. Empty by default. When vectorised, these will become matrices. This is used for the arguments gamma and knots in psurvspline.
scalarargs	Character vector naming scalar arguments of the distribution function that cannot be vectorised. This is used, for example, for the arguments scale and timescale in psurvspline.
...	The remaining arguments define parameters of the distribution pdist. These MUST be named explicitly.

Details

This function is used by default for custom distributions for which an rmst function is not provided.

This assumes a suitably smooth, continuous distribution.

Value

Vector of restricted mean survival times of the distribution at p.

Author(s)

Christopher Jackson <[e-mail]>

Examples

rmst_lnorm(500, start=250, meanlog=7.4225, sdlog = 1.1138)
rmst_generic(plnorm, 500, start=250, meanlog=7.4225, sdlog = 1.1138)
# must name the arguments

chjackson/flexsurv-dev documentation built on April 18, 2024, 6:17 a.m.