1 Introduction | 1.1 Vignette Overview | 1.2 Event Prediction | 1.3 Overview of gestate's scope | 2 Curve-Fitting | 2.1 Example Data Set | 2.2 Curve-Fitting Life-Tables: fit_KM | 2.3 Curve-Fitting Patient-Level Data: fit_tte_data | 3 Event Prediction: event_prediction | 3.1 Introduction | 3.2 Event Curve Fitting | 3.3 Recruitment Specification | 3.4 Censoring Specification | 3.5 Conditioning | 3.6 Accounting for Effects of Adjudication | 3.7 Calculations | 3.8 Plotting Event Prediction | 3.9 Diagnostics: Plotting the Fitted Kaplan Meier Curve | 3.10 Example | 4 Prior Data Integration Approach | 4.1 Introduction | 4.1 Creation of Prior Patient-Level Data: create_tte_prior | 4.2 Weibull Curve Fitting with Weighted Prior Data: fit_tte_data_prior | 4.3 Weibull Event Prediction with Weighted Prior Data: event_prediction_prior | 4.4 Weibull Event Prediction with Weighted Prior Data Example

1 Introduction | 1.1 Vignette Overview | 1.2 Exponential Distributions | 1.3 Non-Proportional Hazards and Complex Censoring | 1.4 Power Calculation vs Sample Size Calculation | 1.5 Shiny App | 2 Curves | 2.1 Event and Censoring Distribution Description and Storage: Curve Objects | 2.2 Recruitment distribution description and storage: RCurve | 3 Analytic Planning of Time-to-Event Trials: nph_traj | 3.1 Getting started: Simple Log-Rank and Hazard-Ratio Calculations | 3.2 Interpreting Output | 3.3 Censoring | 3.4 Other/Advanced Options | 3.5 Planning for Landmark and RMST Analysis | 3.6 Plotting nph_traj Output | 4 Simulating Trial Properties | 4.1 Simulating Data: simulate_trials | 4.2 Simulating Stratified Data: simulate_trials_strata | 4.3 Modifying Simulated Data: set_event_number and set_assess_time | 4.4 Analysing Simulated Data: analyse_sim | 4.5 Summarising Analysed Data: summarise_analysis | 4.6 Comparisons of Simulated Data to Analytic Outputs | 5 References