plotByWeight

This function shows the omni-admissible design -- the design realisation with
the lowest loss score -- from a subset of admissible designs. The input is an
object created by singlearmDesign.

Finds single- and two-arm designs using stochastic curtailment, as described by Law et al. (2022) <doi:10.1080/10543406.2021.2009498> and Law et al. (2021) <doi:10.1002/pst.2067> respectively. Designs can be single-stage or multi-stage. Non-stochastic curtailment is possible as a special case. Desired error-rates, maximum sample size and lower and upper anticipated response rates are inputted and suitable designs are returned with operating characteristics. Stopping boundaries and visualisations are also available. The package can find designs using other approaches, for example designs by Simon (1989) <doi:10.1016/0197-2456(89)90015-9> and Mander and Thompson (2010) <doi:10.1016/j.cct.2010.07.008>. Other features: compare and visualise designs using a weighted sum of expected sample sizes under the null and alternative hypotheses and maximum sample size; visualise any binary outcome design.

Martin Law

curtailment

Finds Binary Outcome Designs Using Stochastic Curtailment

plotByWeight function

<dl><dt>main.output</dt>
<dd>Object created by the function(s) listed above.</dd>
<dt>split.by.cohort.size</dt>
<dd>Logical. If TRUE, creates separate plots for each
cohort size/block size/ number of stages. Defaults to TRUE.</dd></dl>

Arguments

plotByWeight — plotByWeight

<dl>

<dt>main.output</dt>
<dd>Object created by the function(s) listed above.</dd>


<dt>split.by.cohort.size</dt>
<dd>Logical. If TRUE, creates separate plots for each
cohort size/block size/ number of stages. Defaults to TRUE.</dd>

</dl>

plotByWeight: plotByWeight

Description

Usage

Value

Arguments

Examples