optimal_bias_generic

Generic function for optimizing drug development programs with bias adjustment

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_bias_generic function

<dl><dt>adj</dt>
<dd>choose type of adjustment: <code>"multiplicative"</code>,
<code>"additive"</code>, <code>"both"</code> or <code>"all"</code>. When using "both",
<code>res[1,]</code> contains the results using the multiplicative method and <code>res[2,]</code>
contains the results using the additive method. When using "all", there are
also <code>res[3,]</code> and <code>res[4,]</code>, containing the results of a multiplicative
and an additive method which do not only adjust the treatment effect but
also the threshold value for the decision rule.</dd>
<dt>lambdamin</dt>
<dd>minimal multiplicative adjustment parameter lambda (i.e. use estimate with a retention factor)</dd>
<dt>lambdamax</dt>
<dd>maximal multiplicative adjustment parameter lambda (i.e. use estimate with a retention factor)</dd>
<dt>steplambda</dt>
<dd>stepsize for the adjustment parameter lambda</dd>
<dt>alphaCImin</dt>
<dd>minimal additive adjustment parameter alphaCI (i.e. adjust the lower bound of the one-sided confidence interval)</dd>
<dt>alphaCImax</dt>
<dd>maximal additive adjustment parameter alphaCI (i.e. adjust the lower bound of the one-sided confidence interval)</dd>
<dt>stepalphaCI</dt>
<dd>stepsize for alphaCI</dd></dl>

Arguments

Generic function for optimizing drug development programs with bias adjustment — optimal_bias_generic

<dl>

<dt>adj</dt>
<dd>choose type of adjustment: <code>"multiplicative"</code>,
<code>"additive"</code>, <code>"both"</code> or <code>"all"</code>. When using "both",
<code>res[1,]</code> contains the results using the multiplicative method and <code>res[2,]</code>
contains the results using the additive method. When using "all", there are
also <code>res[3,]</code> and <code>res[4,]</code>, containing the results of a multiplicative
and an additive method which do not only adjust the treatment effect but
also the threshold value for the decision rule.</dd>


<dt>lambdamin</dt>
<dd>minimal multiplicative adjustment parameter lambda (i.e. use estimate with a retention factor)</dd>


<dt>lambdamax</dt>
<dd>maximal multiplicative adjustment parameter lambda (i.e. use estimate with a retention factor)</dd>


<dt>steplambda</dt>
<dd>stepsize for the adjustment parameter lambda</dd>


<dt>alphaCImin</dt>
<dd>minimal additive adjustment parameter alphaCI (i.e. adjust the lower bound of the one-sided confidence interval)</dd>


<dt>alphaCImax</dt>
<dd>maximal additive adjustment parameter alphaCI (i.e. adjust the lower bound of the one-sided confidence interval)</dd>


<dt>stepalphaCI</dt>
<dd>stepsize for alphaCI</dd>

</dl>

optimal_bias_generic: Generic function for optimizing drug development programs with bias adjustment

Description

Usage

Arguments