create_prior_data

Assembling information from trials in the source population in a structured way (required as a pre-processing step for MAP prior creation).

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

create_prior_data function

<dl><dt>study_label</dt>
<dd>An optional vector containing trial labels.</dd>
<dt>n_total</dt>
<dd>A vector containing total sample sizes.</dd>
<dt>est</dt>
<dd>A vector containing treatment effect estimates.</dd>
<dt>se</dt>
<dd>A vector containing standard errors of the effect estimates.</dd></dl>

Arguments

Creates input data frame for construction of MAP prior — create_prior_data

<dl>

<dt>study_label</dt>
<dd>An optional vector containing trial labels.</dd>


<dt>n_total</dt>
<dd>A vector containing total sample sizes.</dd>


<dt>est</dt>
<dd>A vector containing treatment effect estimates.</dd>


<dt>se</dt>
<dd>A vector containing standard errors of the effect estimates.</dd>

</dl>

create_prior_data: Creates input data frame for construction of MAP prior

Description

Usage

Value

Arguments

See Also

Examples