hesim_survdists

List of additional distributions for parametric survival analysis that are
not contained in <code>flexsurv</code>. Can be used to fit models with
<code>flexsurv::flexsurvreg()</code>. Same format as <code>flexsurv::flexsurv.dists</code>.

internal

A modular and computationally efficient R package for
parameterizing, simulating, and analyzing health economic simulation
models. The package supports cohort discrete time state transition models
(Briggs et al. 1998) <doi:10.2165/00019053-199813040-00003>,
N-state partitioned survival models (Glasziou et al. 1990)
<doi:10.1002/sim.4780091106>, and individual-level continuous
time state transition models (Siebert et al. 2012) <doi:10.1016/j.jval.2012.06.014>,
encompassing both Markov (time-homogeneous and time-inhomogeneous) and
semi-Markov processes. Decision uncertainty from a cost-effectiveness analysis is
quantified with standard graphical and tabular summaries of a probabilistic
sensitivity analysis (Claxton et al. 2005, Barton et al. 2008) <doi:10.1002/hec.985>,
<doi:10.1111/j.1524-4733.2008.00358.x>. Use of C++ and data.table
make individual-patient simulation, probabilistic sensitivity analysis,
and incorporation of patient heterogeneity fast.

Devin Incerti

hesim

Health Economic Simulation Modeling and Decision Analysis

Jeroen P. Jansen

Mark Clements

R Core Team 

hesim_survdists function

A list with the following elements:<dl>
<dt>name</dt>
<dd>Name of the probability distribution.</dd><dt>pars</dt>
<dd>Vector of strings naming the parameters of the distribution.
These must be the same names as the arguments of the density and probability functions.</dd><dt>location</dt>
<dd>Name of the location parameter.</dd><dt>transforms</dt>
<dd>List of R functions which transform the range of values
taken by each parameter onto the real line. For example,
<code>c(log, log)</code> for a distribution with two positive parameters.</dd><dt>inv.transforms</dt>
<dd>List of R functions defining the corresponding inverse
transformations. Note these must be lists, even for single parameter
distributions they should be supplied as, e.g. <code>c(exp) or list(exp)</code>.</dd><dt>inits</dt>
<dd>A function of the observed survival times <code>t</code> (including
right-censoring times, and using the halfway point for interval-censored times)
which returns a vector of reasonable initial values for maximum likelihood
estimation of each parameter. For example, <code>function(t){ c(1, mean(t)) }</code>
will always initialize the first of two parameters at 1, and the second
(a scale parameter, for instance) at the mean of <code>t</code>.</dd>
</dl>

Format

List of survival distributions — hesim_survdists

A list with the following elements:<dl>
<dt>name</dt>
<dd>Name of the probability distribution.</dd>

<dt>pars</dt>
<dd>Vector of strings naming the parameters of the distribution.
These must be the same names as the arguments of the density and probability functions.</dd>

<dt>location</dt>
<dd>Name of the location parameter.</dd>

<dt>transforms</dt>
<dd>List of R functions which transform the range of values
taken by each parameter onto the real line. For example,
<code>c(log, log)</code> for a distribution with two positive parameters.</dd>

<dt>inv.transforms</dt>
<dd>List of R functions defining the corresponding inverse
transformations. Note these must be lists, even for single parameter
distributions they should be supplied as, e.g. <code>c(exp) or list(exp)</code>.</dd>

<dt>inits</dt>
<dd>A function of the observed survival times <code>t</code> (including
right-censoring times, and using the halfway point for interval-censored times)
which returns a vector of reasonable initial values for maximum likelihood
estimation of each parameter. For example, <code>function(t){ c(1, mean(t)) }</code>
will always initialize the first of two parameters at 1, and the second
(a scale parameter, for instance) at the mean of <code>t</code>.</dd>


</dl>

hesim_survdists: List of survival distributions

Description

Usage

Arguments

Format