plot_trial_trace

Draws three base-R panels showing the predictive probability of
success if accrual stops now, the predictive probability of success at the
maximum sample size, and enrollment and observed events by treatment arm.
Thresholds and early stopping decisions are marked on the probability
panels.

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

plot_trial_trace function

<dl><dt>x</dt>
<dd>A required <code>goldilocks_trial</code> result from <code>survival_adapt()</code>, a
<code>goldilocks_interim</code> result from <code>evaluate_interim()</code>, or an interim trace
data frame.</dd></dl>

Arguments

Plot predictive probabilities and enrollment at interim looks — plot_trial_trace

<dl>

<dt>x</dt>
<dd>A required <code>goldilocks_trial</code> result from <code>survival_adapt()</code>, a
<code>goldilocks_interim</code> result from <code>evaluate_interim()</code>, or an interim trace
data frame.</dd>

</dl>

plot_trial_trace: Plot predictive probabilities and enrollment at interim looks

Description

Usage

Value

Arguments