How uthsc-zhang/rmstpowerboost-package works
RMSTpowerBoost is an R package for calculating power and sample size in RMST-based trials, with an interactive Shiny interface. Users can simulate reference data, calculate power curves, or search for a target-power sample size. The unified interface routes calculations to analytical or bootstrap methods, including stratified, nonlinear GAM, and dependent-censoring approaches. The graph reflects README-documented capabilities and paths in the tree; source excerpts were unavailable, so uncertain internal calls and data handoffs are omitted.
Main entry points: rmst-interface.R Power and sample size · run_app.R App launcher · datagen-interface.R Simulation API · app.R Shiny app
Components 17
User interfaces
- R interface
R/rmst-interface.R - App launcher
R/run_app.R - Shiny app
inst/shiny_app/app.R
Data simulation
- Simulation API
R/datagen-interface.R - Recipe simulation
R/recipe_sim_datagen.R - Recipe generation
R/generate_sets_datagen.R - Recipe loading
R/load_sets_datagen.R
Power and sample size
- Power and sample size
R/rmst-interface.R
Calculation methods
- Linear IPCW analytical
R/linear_ipcw_analytical.R - Linear IPCW bootstrap
R/linear_ipcw_boot.R - Additive stratified
R/additive_stratified_analytical.R - Additive GAM bootstrap
R/additive_gam_boot.R - Multiplicative stratified
R/multiplicative_stratified_analytical.R - Multiplicative bootstrap
R/multiplicative_stratified_boot.R - Dependent censoring
R/dependent_censoring_analytical.R
Other components
- Trial researcher
- Power or sample-size result
R/rmst-interface.R
Connections 14
- Trial researcher → Simulation API: requests simulation
- Simulation API → Recipe simulation: simulates data
- Trial researcher → R interface: requests calculation
- R interface → Power and sample size: routes request
- Power and sample size → Linear IPCW analytical: analytical mode
- Power and sample size → Linear IPCW bootstrap: bootstrap mode
- Power and sample size → Additive stratified: additive strata
- Power and sample size → Additive GAM bootstrap: smooth terms
- Power and sample size → Multiplicative stratified: multiplicative strata
- Power and sample size → Multiplicative bootstrap: multiplicative bootstrap
- Power and sample size → Dependent censoring: dependent censoring
- Power and sample size → Power or sample-size result: returns result
- Trial researcher → App launcher: opens app
- App launcher → Shiny app: launches