print.goldilocks_calendar_summary

Print a calendar-time operating-characteristic summary

Implements Goldilocks adaptive trial designs for time-to-event and
fixed-time binary endpoints. Outcomes are generated with a piecewise
exponential model, with conjugate Gamma priors used for predictive
imputation. Final analyses may use log-rank, Cox, or restricted mean
survival time tests, Bayesian piecewise-exponential inference, frequentist
risk differences, or Bayesian beta-binomial inference. The method closely
follows Broglio and colleagues (2014)
<doi:10.1080/10543406.2014.888569> and supports simulation of design
operating characteristics.

Hickey Graeme L.

goldilocks

Adaptive Trial Designs for Survival and Binary Endpoints

Graeme L. Hickey

Ying Wan

Thevaa Chandereng

Becton, Dickinson and Company 

Tim Kacprowski

print.goldilocks_calendar_summary function

<dl><dt>x</dt>
<dd>A <code>goldilocks_calendar_summary</code> result returned by
<code>summarise_calendar_time()</code>.</dd>
<dt>digits</dt>
<dd>A single non-negative integer giving the number of digits after
the decimal point in displayed values. The default is <code>1</code>.</dd>
<dt>...</dt>
<dd>Additional arguments; currently ignored.</dd></dl>

Arguments

Print a calendar-time operating-characteristic summary — print.goldilocks_calendar_summary

<dl>

<dt>x</dt>
<dd>A <code>goldilocks_calendar_summary</code> result returned by
<code>summarise_calendar_time()</code>.</dd>


<dt>digits</dt>
<dd>A single non-negative integer giving the number of digits after
the decimal point in displayed values. The default is <code>1</code>.</dd>


<dt>...</dt>
<dd>Additional arguments; currently ignored.</dd>

</dl>

print.goldilocks_calendar_summary: Print a calendar-time operating-characteristic summary

Description

Usage

Value

Arguments