os_tte

This function calculate the probability that the endpoint OS is statistically significant.
In the context of cancer research OS stands for overall survival, a positive treatment effect in this endpoints is thus sufficient for a successful program.

internal

Plan optimal sample size allocation and go/no-go decision rules
for phase II/III drug development programs with time-to-event, binary or
normally distributed endpoints when assuming fixed treatment effects or a
prior distribution for the treatment effect, using methods from Kirchner et al.
(2016) <doi:10.1002/sim.6624> and Preussler (2020). Optimal is in the sense of
maximal expected utility, where the utility is a function taking into account
the expected cost and benefit of the program. It is possible to extend to more
complex settings with bias correction (Preussler S et al. (2020)
<doi:10.1186/s12874-020-01093-w>), multiple phase III trials (Preussler et
al. (2019) <doi:10.1002/bimj.201700241>), multi-arm trials (Preussler et al.
(2019) <doi:10.1080/19466315.2019.1702092>), and multiple endpoints
(Kieser et al. (2018) <doi:10.1002/pst.1861>).

Lukas D. Sauer

drugdevelopR

Utility-Based Optimal Phase II/III Drug Development Planning

Stella Erdmann

Johannes Cepicka

Marietta Kirchner

Meinhard Kieser

os_tte function

<dl><dt>HRgo</dt>
<dd>threshold value for the go/no-go decision rule;</dd>
<dt>n2</dt>
<dd>total sample size for phase II; must be even number</dd>
<dt>alpha</dt>
<dd>one- sided significance level</dd>
<dt>beta</dt>
<dd>1-beta power for calculation of the number of events for phase III by Schoenfeld (1981) formula</dd>
<dt>hr1</dt>
<dd>assumed true treatment effect on HR scale for endpoint OS</dd>
<dt>hr2</dt>
<dd>assumed true treatment effect on HR scale for endpoint PFS</dd>
<dt>id1</dt>
<dd>amount of information for <code>hr1</code> in terms of number of events</dd>
<dt>id2</dt>
<dd>amount of information for <code>hr2</code> in terms of number of events</dd>
<dt>fixed</dt>
<dd>choose if true treatment effects are fixed or random</dd>
<dt>rho</dt>
<dd>correlation between the two endpoints</dd>
<dt>rsamp</dt>
<dd>sample data set for Monte Carlo integration</dd></dl>

Arguments

Probability that endpoint OS significant — os_tte

<dl>

<dt>HRgo</dt>
<dd>threshold value for the go/no-go decision rule;</dd>


<dt>n2</dt>
<dd>total sample size for phase II; must be even number</dd>


<dt>alpha</dt>
<dd>one- sided significance level</dd>


<dt>beta</dt>
<dd>1-beta power for calculation of the number of events for phase III by Schoenfeld (1981) formula</dd>


<dt>hr1</dt>
<dd>assumed true treatment effect on HR scale for endpoint OS</dd>


<dt>hr2</dt>
<dd>assumed true treatment effect on HR scale for endpoint PFS</dd>


<dt>id1</dt>
<dd>amount of information for <code>hr1</code> in terms of number of events</dd>


<dt>id2</dt>
<dd>amount of information for <code>hr2</code> in terms of number of events</dd>


<dt>fixed</dt>
<dd>choose if true treatment effects are fixed or random</dd>


<dt>rho</dt>
<dd>correlation between the two endpoints</dd>


<dt>rsamp</dt>
<dd>sample data set for Monte Carlo integration</dd>

</dl>

os_tte: Probability that endpoint OS significant

Description

Usage

Value

Arguments