print.goldilocks_interim

Prints the predictive probabilities and decision from an interim
analysis returned by <code>evaluate_interim()</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_interim function

<dl><dt>x</dt>
<dd>A <code>goldilocks_interim</code> result returned by <code>evaluate_interim()</code>.</dd>
<dt>...</dt>
<dd>Additional arguments; currently ignored.</dd></dl>

Arguments

Print an externally evaluated interim analysis — print.goldilocks_interim

<dl>

<dt>x</dt>
<dd>A <code>goldilocks_interim</code> result returned by <code>evaluate_interim()</code>.</dd>


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

</dl>

print.goldilocks_interim: Print an externally evaluated interim analysis

Description

Usage

Value

Arguments