Bayesian Dynamic Borrowing with Propensity Score

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Documentation for package ‘psborrow’ version 0.2.1

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.clinClass S4 Class for specifying parameters for enrollment time, drop-out pattern and analysis start time
.clinClass-class S4 Class for specifying parameters for enrollment time, drop-out pattern and analysis start time
.covClass S4 Class for setting up covariates
.covClass-class S4 Class for setting up covariates
.eventClass S4 Class for setting parameters for time-to-events
.eventClass-class S4 Class for setting parameters for time-to-events
.priorClass S4 Class for specifying prior distributions and predictors for MCMC methods
.priorClass-class S4 Class for specifying prior distributions and predictors for MCMC methods
apply_mcmc Fit Dynamic Borrowing MCMC Model
c-method Concatenate multiple '.covClasss' classes
c-method Concatenate multiple '.priorClasss' class
extract_samples Fit Dynamic Borrowing MCMC Model
fix_col_names Fix Column Names
get_summary Generate summary statistics of a simulation scenario
is_psborrow_dev Check if user is in psborrow development environment
match_cov Match
plot_bias Plot bias
plot_hr Plot mean posterior hazard ratio between treatment and control
plot_mse Plot mean squared error (MSE)
plot_power Plot power
plot_type1error Plot type 1 error
ps_message Conditional Message
rej_est Generate summary statistics for the MCMC chains
run_mcmc Run MCMC for multiple scenarios with provided data
run_mcmc_p Run MCMC for multiple scenarios with provided data with parallel processing
set_clin Specify parameters for enrollment time, drop-out pattern and analysis start time
set_cov Set up covariates
set_event Set up time-to-events
set_n Simulate external trial indicator and treatment arm indicator
set_prior Specify prior distributions and predictors for MCMC methods
simu_cov Simulate covariates
simu_cov-method Simulate covariates
simu_time Simulate time-to-events for multiple scenarios
summary.apply_mcmc Fit Dynamic Borrowing MCMC Model