Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.

R (>= 3.5.0), coda, ggplot2, MASS, survival
Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models and continuous threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.

Bayesian Hierarchical Models for Basket Trials
person(given = 'Stephan',
family = 'Wojciekowski',
role = c('aut', 'cre'),
email = 'stephan.wojciekowski@boehringer-ingelheim.com')
Provides functions for the evaluation of basket
trial designs with binary endpoints. Operating characteristics of a
basket trial design are assessed by simulating trial data according to
scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and
assessing decision probabilities based on Go / No-go decision making.
The BHMs proposed by
Berry et al. (2013) <doi:10.1177/1740774513497539>
and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>,
as well as a model that combines both approaches are implemented.
Functions are provided to implement Bayesian decision rules as for example
proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>.
In addition, posterior point estimates (mean/median) and credible intervals
for response rates and some model parameters can be calculated.
For simulated trial data, bias and mean squared errors of posterior
point estimates for response rates can be provided.

Provides functions for the evaluation of basket
trial designs with binary endpoints. Operating characteristics of a
basket trial design are assessed by simulating trial data according to
scenarios, analyzing the data with Bayesian hierarchical models (BHMs), and
assessing decision probabilities on stratum and trial-level based on Go / No-go decision making.
The package is build for high flexibility regarding decision rules,
number of interim analyses, number of strata, and recruitment.
The BHMs proposed by
Berry et al. (2013) <doi:10.1177/1740774513497539>
and Neuenschwander et al. (2016) <doi:10.1002/pst.1730>,
as well as a model that combines both approaches are implemented.
Functions are provided to implement Bayesian decision rules as for example
proposed by Fisch et al. (2015) <doi:10.1177/2168479014533970>.
In addition, posterior point estimates (mean/median) and credible intervals
for response rates and some model parameters can be calculated.
For simulated trial data, bias and mean squared errors of posterior
point estimates for response rates can be provided.