d3_skipII_tte

If choosing <code>skipII = TRUE</code>, the program calculates the expected utility for the case when phase
II is skipped and compares it to the situation when phase II is not skipped.
This function calculates the expected sample size for phase III for time-to-event outcomes using a median prior.

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

d3_skipII_tte function

<dl><dt>alpha</dt>
<dd>significance level</dd>
<dt>beta</dt>
<dd><code>1-beta</code> power for calculation of sample size for phase III</dd>
<dt>median_prior</dt>
<dd>the median_prior is given as -log(hr1), the assumed true treatment effect</dd></dl>

Arguments

Expected probability to go to phase III for time-to-event outcomes — d3_skipII_tte

<dl>

<dt>alpha</dt>
<dd>significance level</dd>


<dt>beta</dt>
<dd><code>1-beta</code> power for calculation of sample size for phase III</dd>


<dt>median_prior</dt>
<dd>the median_prior is given as -log(hr1), the assumed true treatment effect</dd>

</dl>

d3_skipII_tte: Expected probability to go to phase III for time-to-event outcomes

Description

Usage

Value

Arguments

Examples