beastt0.0.3 package

Bayesian Evaluation, Analysis, and Simulation Software Tools for Trials

approx_mvn_at_time

Approximate Multivariate Normal Distribution as Beta at a Specific Tim...

avg_dist

Calculate Average Distribution from Multiple Distributional Objects

beastt-package

The 'beastt' package.

bootstrap_cov

Bootstrap Covariate Data

calc_cond_binary

Calculate Conditional Drift and Treatment Effect for Binary Outcome Mo...

calc_cond_weibull

Calculate Conditional Drift and Treatment Effect for Time-to-Event Out...

calc_post_beta

Calculate Posterior Beta

calc_post_norm

Calculate Posterior Normal

calc_post_weibull

Calculate Posterior Weibull

calc_power_prior_beta

Calculate Power Prior Beta

calc_power_prior_norm

Calculate Power Prior Normal

calc_power_prior_weibull

Calculate Power Prior Weibull

calc_prop_scr

Create a Propensity Score Object

calc_study_duration

Calculate the Analysis Time Based on a Target Number of Events and/or ...

inv_logit

Inverse Logit Function

is_prop_scr

Test If Propensity Score Object

mix_means

Extract Means of Mixture Components

mix_sigmas

Extract Standard Deviations of Mixture Components

plot_dist

Plot Distribution

prop_scr_cloud

Propensity Score Cloud Plot

prop_scr_dens

Density of the Propensity Score Object

prop_scr_hist

Histogram of the Propensity Score Object

prop_scr_love

Love Plot of the Absolute Standardized Mean Differences

reexports

Objects exported from other packages

rescale_ps

Rescale a prop_scr object

robustify_mvnorm

Robustify Multivariate Normal Distributions

robustify_norm

Robustify Normal Distributions

sim_accrual

Simulate Participant Accrual Times

sim_pw_const_haz

Simulate Event Times for Each Individual from a Piecewise Constant Haz...

sim_weib_ph

Simulate Event Times for Each Participant from a Weibull Proportional ...

sweet_spot_plot

Create Sweet Spot Plots for Multiple Simulation Scenarios

tidy.prop_scr

Tidy a(n) prop_scr object

trim_ps

Trim a prop_scr object

Bayesian dynamic borrowing with covariate adjustment via inverse probability weighting for simulations and data analyses in clinical trials. This makes it easy to use propensity score methods to balance covariate distributions between external and internal data. This methodology based on Psioda et al (2025) <doi:10.1080/10543406.2025.2489285>.

  • Maintainer: Christina Fillmore
  • License: GPL (>= 3)
  • Last published: 2025-05-15