optimal_generic

Generic function for optimizing drug development programs

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

optimal_generic function

<dl><dt>c2</dt>
<dd>variable per-patient cost for phase II</dd>
<dt>c3</dt>
<dd>variable per-patient cost for phase III</dd>
<dt>c02</dt>
<dd>fixed cost for phase II</dd>
<dt>c03</dt>
<dd>fixed cost for phase III</dd>
<dt>K</dt>
<dd>constraint on the costs of the program, default: <code>Inf</code>, e.g. no constraint</dd>
<dt>N</dt>
<dd>constraint on the total expected sample size of the program, default: <code>Inf</code>, e.g. no constraint</dd>
<dt>S</dt>
<dd>constraint on the expected probability of a successful program, default: <code>-Inf</code>, e.g. no constraint</dd>
<dt>b1</dt>
<dd>expected gain for effect size category "small"</dd>
<dt>b2</dt>
<dd>expected gain for effect size category "medium"</dd>
<dt>b3</dt>
<dd>expected gain for effect size category "large"</dd>
<dt>num_cl</dt>
<dd>number of clusters used for parallel computing, default: 1</dd></dl>

Arguments

Generic function for optimizing drug development programs — optimal_generic

<dl>

<dt>c2</dt>
<dd>variable per-patient cost for phase II</dd>


<dt>c3</dt>
<dd>variable per-patient cost for phase III</dd>


<dt>c02</dt>
<dd>fixed cost for phase II</dd>


<dt>c03</dt>
<dd>fixed cost for phase III</dd>


<dt>K</dt>
<dd>constraint on the costs of the program, default: <code>Inf</code>, e.g. no constraint</dd>


<dt>N</dt>
<dd>constraint on the total expected sample size of the program, default: <code>Inf</code>, e.g. no constraint</dd>


<dt>S</dt>
<dd>constraint on the expected probability of a successful program, default: <code>-Inf</code>, e.g. no constraint</dd>


<dt>b1</dt>
<dd>expected gain for effect size category "small"</dd>


<dt>b2</dt>
<dd>expected gain for effect size category "medium"</dd>


<dt>b3</dt>
<dd>expected gain for effect size category "large"</dd>


<dt>num_cl</dt>
<dd>number of clusters used for parallel computing, default: 1</dd>

</dl>

optimal_generic: Generic function for optimizing drug development programs

Description

Usage

Arguments