Student-class

Student

quantile,Student-method

simulate,Student,numeric-method

Implements exact t-distributions instead of a normal approximation

Optimize one or two-arm, two-stage designs for clinical trials with
respect to several implemented objective criteria or custom objectives.
Optimization under uncertainty and conditional (given stage-one outcome)
constraints are supported.
See Pilz et al. (2019) <doi:10.1002/sim.8291> and
Kunzmann et al. (2021) <doi:10.18637/jss.v098.i09> for details.

Maximilian Pilz

adoptr

Adaptive Optimal Two-Stage Designs

Kevin Kunzmann

Jan Meis

Nico Bruder

Student-class function

<dl><dt>two_armed</dt>
<dd>logical indicating if a two-armed trial is regarded</dd>
<dt>x</dt>
<dd>outcome</dd>
<dt>probs</dt>
<dd>vector of probabilities</dd>
<dt>n</dt>
<dd>sample size</dd>
<dt>theta</dt>
<dd>distribution parameter</dd>
<dt>...</dt>
<dd>further optional arguments</dd>
<dt>object</dt>
<dd>object of class <code>Student</code></dd>
<dt>nsim</dt>
<dd>number of simulation runs</dd>
<dt>seed</dt>
<dd>random seed</dd></dl>

Arguments

Student's t data distribution — Student-class

<dl>

<dt>two_armed</dt>
<dd>logical indicating if a two-armed trial is regarded</dd>


<dt>x</dt>
<dd>outcome</dd>


<dt>probs</dt>
<dd>vector of probabilities</dd>


<dt>n</dt>
<dd>sample size</dd>


<dt>theta</dt>
<dd>distribution parameter</dd>


<dt>...</dt>
<dd>further optional arguments</dd>


<dt>object</dt>
<dd>object of class <code>Student</code></dd>


<dt>nsim</dt>
<dd>number of simulation runs</dd>


<dt>seed</dt>
<dd>random seed</dd>

</dl>

Student-class: Student's t data distribution

Description

Usage

Arguments

See Also

Examples