Package: RMSTpowerBoost 1.0.3

Arnab Aich

RMSTpowerBoost: Power and Sample Size for Restricted Mean Survival Time Based Clinical Trials

Tools for Restricted Mean Survival Time based study design and analysis planning. Provides power and sample size calculations for two-arm studies using direct modeling approaches from the literature, including semiparametric additive models, linear Inverse Probability Weighting based models from Wei (2014) <doi:10.1093/biostatistics/kxt050>, multiplicative stratified models from Wang (2019) <doi:10.1002/sim.8356>, and covariate-dependent censoring methods from Wang (2018) <doi:10.1007/s10985-017-9391-6>.

Authors:Arnab Aich [aut, cre], Yuan Zhang [aut]

RMSTpowerBoost_1.0.3.tar.gz
RMSTpowerBoost_1.0.3.zip(r-4.7)RMSTpowerBoost_1.0.3.zip(r-4.6)RMSTpowerBoost_1.0.3.zip(r-4.5)
RMSTpowerBoost_1.0.3.tgz(r-4.6-any)RMSTpowerBoost_1.0.3.tgz(r-4.5-any)
RMSTpowerBoost_1.0.3.tar.gz(r-4.7-any)RMSTpowerBoost_1.0.3.tar.gz(r-4.6-any)
RMSTpowerBoost_1.0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RMSTpowerBoost/json (API)

# Install 'RMSTpowerBoost' in R:
install.packages('RMSTpowerBoost', repos = c('https://uthsc-zhang.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/uthsc-zhang/rmstpowerboost-package/issues

Pkgdown/docs site:https://uthsc-zhang.github.io

Datasets:

On CRAN:

Conda:

bootsrapfrequentist-methodspowerrmstsamplesizeshinyappssurvival-analysis

5.81 score 3 scripts 483 downloads 31 exports 42 dependencies

Last updated from:0d1179a9b0. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK258
source / vignettesOK217
linux-release-x86_64OK278
macos-release-arm64OK234
macos-oldrel-arm64OK205
windows-develOK216
windows-releaseOK215
windows-oldrelOK233
wasm-releaseOK146

Exports:additive.power.analyticaladditive.ss.analyticalcovar_binarycovar_categoricalcovar_continuousDC.power.analyticalDC.ss.analyticaldescribe_generationGAM.power.bootGAM.ss.bootgen_covariatesgenerate_recipe_setslinear.power.analyticallinear.power.bootlinear.ss.analyticallinear.ss.bootload_recipe_setsMS.power.analyticalMS.power.bootMS.ss.analyticalMS.ss.bootrebuild_manifestrecipe_gridrecipe_quick_aftrecipe_quick_phrmst.powerrmst.simrmst.ssrun_appsimulate_from_recipevalidate_recipe

Dependencies:clicodetoolscpp11digestdplyrevaluatefarverfuturefuture.applygenericsggplot2globalsgluegtablehighrisobandknitrlabelinglatticelifecyclelistenvmagrittrMatrixmgcvnlmeparallellypillarpkgconfigR6RColorBrewerrlangS7scalessurvivaltibbletidyselectutf8vctrsviridisLitewithrxfunyaml

Data Generation: Mechanisms and Examples
Quick start with rmst.sim() | What this covers | Recipe skeleton | Covariates | Treatment | Event-time engines | Censoring | Examples | Target overall censoring | Explicit mix (administrative + random) | Explicit covariate-dependent censoring | Using a censoring recipe inside a full simulation | Worked examples | Example 1 — AFT Lognormal | Example 2 — AFT Weibull | Example 3 — PH Exponential (single segment) | Example 4 — PH Piecewise Exponential (multi-segment) | Generate data based on formula (event & censoring) | Batch generation with metadata | Reproducibility tips
Rendered fromRMSTpowerBoost-DataGen.Rmdusingknitr::rmarkdown

Last update: 2026-04-22
Started: 2025-10-02

RMSTpowerBoost: Sample Size and Power Calculations for RMST-based Clinical Trials
Introduction | Core Concepts of RMSTpowerBoost Package | The Analytic Method | The Bootstrap Method | The Sample Size Search Algorithm | The Unified Interface | Selecting an Appropriate Model | Linear IPCW Models | Theory and Model | Analytical Methods | Power Calculation | Sample Size Calculation | Bootstrap Methods | Power and Sample Size Calculation (bootstrap) | Additive Stratified Models | Multiplicative Stratified Models | Power Calculation (bootstrap) | Sample Size Calculation (bootstrap) | Semiparametric GAM Models | Power Calculation Formula | Covariate-Dependent Censoring Models | Sample-Size Calculation | Interactive Shiny Application | Accessing the Application | App Features | Conclusion | References
Rendered fromRMSTpowerBoost-Main.Rmdusingknitr::rmarkdown

Last update: 2026-04-22
Started: 2025-10-02

User Guide for the RMSTpowerBoost Shiny Application
Introduction | Workflow Overview | Sidebar Controls | Step 1. Data Source | Step 2. Generation | Step 2. MICE / Cleaning | Step 3. Model and Mapping | Step 4. Analysis | Main Panel Tabs | Pipeline | Data | Summary | KM Plot | Analysis | Run Log | About | Export Behavior
Rendered fromRMSTpowerBoost-App.Rmdusingknitr::rmarkdown

Last update: 2026-04-22
Started: 2025-10-02

RMSTpowerBoost Home
RMSTpowerBoost | Guides | Key Features | Installation | Shiny App
Rendered fromindex.Rmdusingknitr::rmarkdown

Last update: 2026-04-20
Started: 2026-04-17

Readme and manuals

Help Manual

Help pageTopics
Analyze Power for a Stratified Additive RMST Model (Analytic)additive.power.analytical
Find Sample Size for a Stratified Additive RMST Model (Analytic)additive.ss.analytical
Simulated dataset: AFT log-normal, L = 12, n = 150aft_lognormal_L12_n150
Simulated dataset: AFT Weibull, L = 24, n = 200aft_weibull_L24_n200
Binary covariate definitioncovar_binary
Categorical covariate definitioncovar_categorical
Continuous covariate definitioncovar_continuous
Analyze Power for RMST Model with Covariate-Dependent Censoring (Analytic)DC.power.analytical
Find Sample Size for RMST with Covariate-Dependent Censoring (Analytic)DC.ss.analytical
Summarize a generated dataset and its simulation mechanismdescribe_generation
Calculate Power for a Semiparametric Additive RMST Model via SimulationGAM.power.boot
Find Sample Size for a Semiparametric Additive RMST Model via SimulationGAM.ss.boot
Generate covariate matrix/data frame from a recipegen_covariates
Generate simulated datasets across scenario combinations (TXT/CSV/RDS/RData)generate_recipe_sets
Analyze Power for a Linear RMST Model (Analytic)linear.power.analytical
Analyze Power for a Linear RMST Model via Simulationlinear.power.boot
Find Sample Size for a Linear RMST Model (Analytic)linear.ss.analytical
Find Sample Size for a Linear RMST Model via Simulationlinear.ss.boot
Load datasets from a recipe-sets manifestload_recipe_sets
Analyze Power for a Multiplicative Stratified RMST Model (Analytic)MS.power.analytical
Analyze Power for a Multiplicative Stratified RMST Model via SimulationMS.power.boot
Find Sample Size for a Multiplicative Stratified RMST Model (Analytic)MS.ss.analytical
Estimate Sample Size for a Multiplicative Stratified RMST Model via SimulationMS.ss.boot
Simulated dataset: PH piecewise-exponential, L = 18, n = 250ph_pwexp_L18_n250
Simulated dataset: PH Weibull, L = 24, n = 300ph_weibull_L24_n300
Print an rmst_power resultplot.rmst_power print.rmst_power summary.rmst_power
Print an rmst_ss resultplot.rmst_ss print.rmst_ss summary.rmst_ss
Rebuild manifest for an existing output directory (no re-simulation)rebuild_manifest
Expand a recipe over a grid of values (list-only)recipe_grid
Quick AFT recipe builder for list-based simulation recipesrecipe_quick_aft
Quick PH recipe builderrecipe_quick_ph
Power analysis for RMST-based models via formula interfacermst.power
Simulate survival data for RMST analysisrmst.sim
Sample size estimation for RMST-based models via formula interfacermst.ss
Launch the RMSTpowerBoost Shiny Applicationrun_app
Simulate a dataset from a validated recipe (list-only)simulate_from_recipe
Validate a simulation recipe (list-only schema)validate_recipe