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OncoBayes2 (version 0.6-0)

Bayesian Logistic Regression for Oncology Dose-Escalation Trials

Description

Bayesian logistic regression model with optional EXchangeability-NonEXchangeability parameter modelling for flexible borrowing from historical or concurrent data-sources. The safety model can guide dose-escalation decisions for adaptive oncology Phase I dose-escalation trials which involve an arbitrary number of drugs. Please refer to Neuenschwander et al. (2008) and Neuenschwander et al. (2016) for details on the methodology.

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Version

Install

install.packages('OncoBayes2')

Monthly Downloads

466

Version

0.6-0

License

GPL (>= 3)

Maintainer

Sebastian Weber

Last Published

February 11th, 2020

Functions in OncoBayes2 (0.6-0)

Bind rows of multiple data frames with zero fill

Three-drug combination example

Update data of a BLRM analysis

example-single-agent

Single Agent Example

update.blrm_trial

Update data and/or prior of a BLRM trial

example-combo2_trial

Two-drug combination example using BLRM Trial

Two-drug combination example

.get_strata_group_fct

extracts from a blrmfit object and a given data-set the group and stratum factor

dose_info_combo2

Dataset: trial dose information for a dual-agent combination study

Dose-Escalation Trials guided by Bayesian Logistic Regression Model

predictive_interval.blrmfit

Posterior predictive intervals

prior_summary.blrmfit

Summarise model prior

drug_info_combo2

Dataset: drug information for a dual-agent combination study

Dataset: historical data on two single-agents to inform a combination study

Dataset: historical and concurrent data on a three-way combination

summary.blrm_trial

Summarise trial

summary.blrmfit

Summarise model results

Single-agent example

posterior_interval.blrmfit

Posterior intervals

Runs example models

blrm_formula_linear

Build a BLRM formula with linear interaction term in logit-space

posterior_linpred.blrmfit

Posterior of linear predictor

nsamples.blrmfit

Return the number of posterior samples

Dataset: historical and concurrent data on a two-way combination

posterior_predict.blrmfit

Posterior of predictive

Numerically stable summation of log inv logit

Internal function to simulate from the posterior new parameter draws

Utility function to label parameter indices according to factor levels.

Bayesian Logistic Regression Model for N-compounds with EXNEX