ExpQuantOS

Quantile function for OS survival function induced by an illness-death model

Based on the illness-death model a large number of clinical
trials with oncology endpoints progression-free survival (PFS) and
overall survival (OS) can be simulated, see Meller, Beyersmann and
Rufibach (2019) <doi:10.1002/sim.8295>. The simulation set-up allows
for random and event-driven censoring, an arbitrary number of
treatment arms, staggered study entry and drop-out. Exponentially,
Weibull and piecewise exponentially distributed survival times can be
generated. The correlation between PFS and OS can be calculated.

Alexandra Erdmann

simIDM

Simulating Oncology Trials using an Illness-Death Model

Kaspar Rufibach

Holger Löwe

Daniel Sabanés Bové

F. Hoffmann-La Roche AG 

University of Ulm 

ExpQuantOS function

<dl><dt>q</dt>
<dd>(<code>numeric</code>) quantile(s) at which to compute event time (q = 1 / 2 corresponds to median).</dd>
<dt>h01</dt>
<dd>(<code>numeric vector</code>) constant transition hazards for 0 to 1 transition.</dd>
<dt>h02</dt>
<dd>(<code>numeric vector</code>) constant transition hazards for 0 to 2 transition.</dd>
<dt>h12</dt>
<dd>(<code>numeric vector</code>) constant transition hazards for 1 to 2 transition.</dd></dl>

Arguments

Quantile function for OS survival function induced by an illness-death model — ExpQuantOS

<dl>

<dt>q</dt>
<dd>(<code>numeric</code>) quantile(s) at which to compute event time (q = 1 / 2 corresponds to median).</dd>


<dt>h01</dt>
<dd>(<code>numeric vector</code>) constant transition hazards for 0 to 1 transition.</dd>


<dt>h02</dt>
<dd>(<code>numeric vector</code>) constant transition hazards for 0 to 2 transition.</dd>


<dt>h12</dt>
<dd>(<code>numeric vector</code>) constant transition hazards for 1 to 2 transition.</dd>

</dl>

ExpQuantOS: Quantile function for OS survival function induced by an illness-death model

Description

Usage

Value

Arguments

Examples