Bayesian Dynamic Borrowing Analysis and Simulation
Add Covariates for Model Adjustment
Analysis Class
Coerce a psborrow2 object to a data frame
Specify Correlated Baseline Covariates
Baseline Data Frame Object
Baseline Data Frame List
BaselineObject class for data simulation
Legacy function for the bernoulli prior
Legacy function for the beta prior
Check Data Matrix for Required Columns
Create a Fixed External Data Object
Create continuous covariate
ContinuousOutcome class
Create Covariance Matrix
Covariate Class
Create alpha string
Compile MCMC sampler using STAN and create simulation object
Create tau string
Create a DataSimEnrollment Object
Cut Off Functions
Cut Off Object
Enrollment Object
Event Time Distribution Object
Fixed External Control Data Object
Data Simulation Object Class
Constant Enrollment Rates
Evaluate constraints
Legacy function for the exponential survival distribution
Legacy function for the exponential prior
Legacy function for the gamma prior
Generate Data for a BaselineObject
Generate Data for a DataSimObject
Generate Data from Object
Get CmdStanModel objects for MCMCSimulationResults
Get Simulated Data from SimDataList object
Get prior string for all covariates
Get Quantiles of Random Data
Get results for MCMCSimulationResults objects
Get method for Stan model
Get Variables
Legacy function for the half-cauchy prior
Legacy function for the normal half prior
Load and interpolate Stan model
Load a Stan psborrow2 template
Legacy function for binary logistic regression
Sample from Stan model
MCMCSimulationResult Class
Legacy function for the normal prior
PriorBeta Class
PriorCauchy Class
PriorExponential Class
PriorGamma Class
PriorHalfCauchy Class
PriorHalfNormal Class
PriorNormal Class
PriorPoisson Class
psborrow2: Bayesian Dynamic Borrowing Analysis and Simulation
Rename Covariates in draws Object
Set Clinical Cut Off Rule
Set Drop Out Distribution
Set Enrollment Rates for Internal and External Trials
Set Transformations in Baseline Objects
Set transformations in BaselineObject objects
Show guide for objects with guides
Input borrowing details for a simulation study
Input covariate adjustment details for a simulation study
Summarize the number of continuous and binary covariates in a `SimCova...
SimDataList Class
SimOutcomeList Class
SimSampleSize Class
SimTreatmentList Class
Simulation Class
SimVar Class
SimVarBin class
SimVarCont class
TimeToEvent class
Specify Treatment Details
Treatment Class
Trim columns from Data Matrix Based on Borrowing object type
Trim Rows from Data Matrix Based on Borrowing object type
Prior uniform distribution
UniformPrior Class
Create Variable Dictionary
Legacy function for the Weibull proportional Hazards survival distribu...
SimCovariates Class
BorrowingNone class
Build the model string by interpolating the Stan template
Combine objects in psborrow2
Legacy function for the cauchy prior
Check Stan
Prior beta distribution
Prior cauchy distribution
Prior exponential distribution
Prior gamma distribution
Prior half-cauchy distribution
Prior half-normal distribution
Prior normal distribution
Prior poisson distribution
Borrowing Class
BorrowingFull class
BorrowingHierarchicalCommensurate class
Create binary covariate
Binary Cut-Off Transformation
BinaryOutcome class
Legacy function for specifying borrowing details
Full borrowing
Hierarchical commensurate borrowing
No borrowing
Compile MCMC sampler using STAN and create analysis object
Create Baseline Data Simulation Object
Create Data Matrix
Data Simulation
Specify a Time to Event Distribution
Bernoulli distribution with logit parametrization
Normal Outcome Distribution
Exponential survival distribution
Piecewise exponential survival distribution
Prior Class
Weibull survival distribution (proportional hazards formulation)
Outcome class
OutcomeBinaryLogistic class
OutcomeContinuousNormal class
OutcomeSurvExponential Class
PriorBernoulli Class
OutcomeSurvPEM Class
OutcomeSurvWeibullPH Class
Plot Probability Density Function Values
Plot Probability Mass Function Values
Plot Prior Objects
Legacy function for the poisson prior
Get All Variable Names in Simulated Data Model Matrix
Prior bernoulli distribution
Specify covariates for simulation study
Input generated data for a simulation study
Input outcome details for a simulation study
Set simulation study parameters for sample size
Input treatment details for a simulation study
SimBorrowingList Class
SimCovariateList Class
Bayesian dynamic borrowing is an approach to incorporating external data to supplement a randomized, controlled trial analysis in which external data are incorporated in a dynamic way (e.g., based on similarity of outcomes); see Viele 2013 <doi:10.1002/pst.1589> for an overview. This package implements the hierarchical commensurate prior approach to dynamic borrowing as described in Hobbes 2011 <doi:10.1111/j.1541-0420.2011.01564.x>. There are three main functionalities. First, 'psborrow2' provides a user-friendly interface for applying dynamic borrowing on the study results handles the Markov Chain Monte Carlo sampling on behalf of the user. Second, 'psborrow2' provides a simulation framework to compare different borrowing parameters (e.g. full borrowing, no borrowing, dynamic borrowing) and other trial and borrowing characteristics (e.g. sample size, covariates) in a unified way. Third, 'psborrow2' provides a set of functions to generate data for simulation studies, and also allows the user to specify their own data generation process. This package is designed to use the sampling functions from 'cmdstanr' which can be installed from <https://stan-dev.r-universe.dev>.
Useful links