get_model_input_1exp

Transform cumulative probabilities to fit beta distributions

Tipping point analysis for clinical trials that employ Bayesian dynamic borrowing via robust meta-analytic predictive (MAP) priors. Further functions facilitate expert elicitation of a primary weight of the informative component of the robust MAP prior and computation of operating characteristics. Intended use is the planning, analysis and interpretation of extrapolation studies in pediatric drug development, but applicability is generally wider.

Christian Stock

tipmap

Tipping Point Analysis for Bayesian Dynamic Borrowing

Morten Dreher

Emma Torrini

 Boehringer Ingelheim Pharma GmbH & Co. KG

get_model_input_1exp function

<dl><dt>cum_probs</dt>
<dd>Numeric vector, containing cumulative probabilities of weights for one expert, as elicited through the roulette method. Each element of the vector represents one bin in the grid.</dd>
<dt>w</dt>
<dd>Numeric vector, upper interval limit of bin (defaults to <code>1:length(cum_probs) / length(cum_probs)</code>).</dd></dl>

Arguments

Transform cumulative probabilities to fit beta distributions — get_model_input_1exp

<dl>

<dt>cum_probs</dt>
<dd>Numeric vector, containing cumulative probabilities of weights for one expert, as elicited through the roulette method. Each element of the vector represents one bin in the grid.</dd>


<dt>w</dt>
<dd>Numeric vector, upper interval limit of bin (defaults to <code>1:length(cum_probs) / length(cum_probs)</code>).</dd>

</dl>

get_model_input_1exp: Transform cumulative probabilities to fit beta distributions

Description

Usage

Value

Arguments

See Also

Examples