survPFSOS

Survival Function of the Product PFS*OS for Different Transition Models

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 

survPFSOS function

<dl><dt>t</dt>
<dd>(<code>numeric</code>) time at which the value of the PFS*OS survival function is to be computed.</dd>
<dt>transition</dt>
<dd>(<code>TransitionParameters</code>) 
see <code>exponential_transition()</code>, <code>weibull_transition()</code> or <code>piecewise_exponential()</code> for details.</dd></dl>

Arguments

Survival Function of the Product PFS*OS for Different Transition Models — survPFSOS

<dl>

<dt>t</dt>
<dd>(<code>numeric</code>) time at which the value of the PFS*OS survival function is to be computed.</dd>


<dt>transition</dt>
<dd>(<code>TransitionParameters</code>) 
see <code>exponential_transition()</code>, <code>weibull_transition()</code> or <code>piecewise_exponential()</code> for details.</dd>

</dl>

survPFSOS: Survival Function of the Product PFS*OS for Different Transition Models

Description

Usage

Value

Arguments

Examples