print.goldilocks_trial

Prints the final trial summary and reports how many interim
looks were completed for a <code>goldilocks_trial</code> object returned by
<code>survival_adapt()</code>.

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_trial function

<dl><dt>x</dt>
<dd>A <code>goldilocks_trial</code> result returned by <code>survival_adapt()</code> when
<code>return_trace = TRUE</code>.</dd>
<dt>...</dt>
<dd>Additional arguments passed to <code><a href="/link/base%3A%3Aprint.data.frame()?package=goldilocks&version=1.0.0" data-mini-rdoc="goldilocks::base::print.data.frame()">base::print.data.frame()</a></code> when
printing the final trial summary.</dd></dl>

Arguments

Print a Goldilocks adaptive trial result — print.goldilocks_trial

<dl>

<dt>x</dt>
<dd>A <code>goldilocks_trial</code> result returned by <code>survival_adapt()</code> when
<code>return_trace = TRUE</code>.</dd>


<dt>...</dt>
<dd>Additional arguments passed to <code><a href='https://rdrr.io/r/base/print.dataframe.html'>base::print.data.frame()</a></code> when
printing the final trial summary.</dd>

</dl>

print.goldilocks_trial: Print a Goldilocks adaptive trial result

Description

Usage

Value

Arguments