Bayesian Logistic Regression for Oncology Dose-Escalation Trials
Bind rows of multiple data frames with zero fill
Bayesian Logistic Regression Model for N-compounds with EXNEX
Build a BLRM formula with linear interaction term in logit-space
Build a BLRM formula with saturating interaction term in logit-space
Dose-Escalation Trials guided by Bayesian Logistic Regression Model
Critical quantile
Extract Diagnostic Quantities of OncoBayes2 Models
extracts from a blrmfit object and a given data-set the group and stra...
Obtain design matrices.
Utility function to label parameter indices according to factor levels...
Test if each group is assigned to exactly 1 stratum. Error otherwise.
Transform blrmfit or blrm_trial to draws objects
Runs example models
Two-drug combination example using BLRM Trial
Two-drug combination example
Three-drug combination example
Single Agent Example
Logit (log-odds) and inverse-logit function.
Numerically stable summation of log inv logit
Numerically stable mean of logs
Return the number of posterior samples
OncoBayes2
Plot a fitted model
Posterior intervals
Posterior of linear predictor
Posterior of predictive
Internal function to simulate from the posterior new parameter draws
Posterior predictive intervals
Summarise model prior
Summarise trial
Summarise model results
Update data and/or prior of a BLRM trial
Update data of a BLRM analysis
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) <doi:10.1002/sim.3230> and Neuenschwander et al. (2016) <doi:10.1080/19466315.2016.1174149> for details on the methodology.