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

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

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

RMSTpowerBoost | Guides | Key Features | Installation | Shiny App