Trial overview | Adaptive decision process | Endpoint scale | SAP event-time models and time units | Effectiveness endpoint | Safety endpoint | A reusable design specification | Graphical assessment of the simulated design | A fuller simulation template | SAP sensitivity assumptions and reference benchmarks | References

Clinical question and planned analysis | Planned design versus operational history | Source and status of every modeled input | Event-time and accrual assumptions | A one-trial goldilocks approximation | Small null and alternative simulation | Differences from the sponsor's design | References

Overview | From assumptions to operating characteristics | Data-generating assumptions | Interim prediction | Bayesian survival calculation | Completed-data analysis | Summaries for design evaluation | Prespecification and reproducibility | Implementation map

Trial and endpoint | The reported stopping rules | The four-way goldilocks rule | Source and status of modeled inputs | Mapping the longitudinal analysis model | Published design-generating assumptions | Assumptions versus the reported trial history | One simulated trial | Small operating-characteristic demonstration | Operating-characteristic evaluation | What this example does and does not reproduce | Sources and document roles

Design assumptions | Final analysis and hypotheses | One-sided tests | Simulation settings | Power and type I error | Calibrating the final threshold | Calendar time and follow-up | Graphical assessment | References

Two-arm design | Choosing the binary imputation approach | Single-arm design | Choosing bin_method | Operating characteristics

Choosing the analysis | Example design | Pending and missing endpoint outcomes | Reference

Overview | 1. Design and notation | 2. Continuous-time enrollment process | 3. Event-time model | 3.1 Independent dropout and administrative censoring | 4. Posterior distribution of hazards | $$\pi(\boldsymbol{\lambda} \mid \mathcal{D}) | 5. Predictive distribution for incomplete outcomes | $$p(\mathcal{D}^{\mathrm{mis}} \mid \mathcal{D}_{\ell}^{\mathrm{obs}}) | 6. Interim decision rule | 6.1 Predictive probability at the current sample size | $$P_ | 6.2 Predictive probability at the maximum sample size | $$P_ | 7. Final analysis | 7.1 Frequentist final tests | 7.2 Bayesian survival final test | $$\Delta^ | \left[1 - \exp{-H_1^{(b)}(\tau)}\right] | $$\widehat{\Pr}(\Delta < h_0 \mid \mathcal{D}) | 7.3 Bayesian binary final test | 7.4 Loss to follow-up at the final analysis | 8. Operating characteristics | 8.1 Graphical assessment | 9. Threshold selection | 10. Relation to group-sequential designs | 11. Scope and statistical limitations | References

Preparing the data cut | Evaluating the look | Using separate predictive and analysis priors | Using a prespecified RMST endpoint | Reviewing and retaining the audit trail

When piecewise hazards help | Setting up the design | The Bayesian decision rule | What a simulated trial dataset looks like | A single simulated trial | Notes on the piecewise model | Sensitivity to the cut-point specification | See also

The decision rule | Setting up the design | Operating characteristics | A practical caveat on benchmarks | See also

Design and null scenario | Screening a candidate grid | Plotting the calibration curve | Independent validation | Extending the calibration | Calibrating an RMST design

Reviewing one trial's interim history | Visualizing the enrollment plan | Plotting the interim decision path | Summarizing many simulated trials

Effect and hypothesis | A trial with a delayed treatment effect | Prediction and loss to follow-up | Evaluate the design | References

Example | Background | Data | Model | Conclusion | Acknowledgements | References

Description | Unbalanced and balanced data formats | Motivating Examples | The heart.valve data-set | The liver data | The mental data-set | The epileptic data-set | The aids data-set | Converting between balanced and unbalanced data-formats | Creating a jointdata object | Plotting a jointdata object | Exploring covariance structure | Model-fitting | The random effects model | Acknowledgements | References

Problem | Simulation | Grid search | References

Example | Simulations

Introduction | Linear Regresion Model Background | Estimation of the historical data weight | Discount function | Estimation of the posterior distribution of the current data, conditional on the historical data | Inputting Data | Examples | Two-arm trial

Introduction | Estimation of the historical data weight | Discount function | Estimation of the posterior distribution of the current data, conditional on the historical data | Estimation of the posterior treatment effect: treatment versus control | Inputting Data | Examples | One-arm trial | Two-arm trial

Introduction | Estimation of the historical data weight | Discount function | Estimation of the posterior distribution of the current data, conditional on the historical data | Estimation of the posterior treatment effect: treatment versus control | Inputting Data | Examples | One-arm trial | Two-arm trial

Introduction | Piecewise Exponential Model Background | Estimation of the historical data weight | Estimation under a one-arm analysis | Estimation under a two-arm analysis | Discount function | Estimation of the posterior distribution of the current data, conditional on the historical data | Inputting Data | Examples | One-arm trial | Two-arm trial

Introduction | Heart valve data | Data | Model fitting | Post-fit analysis | Bootstrap standard errors | Univariate joint models: joineRML versus joineR

Introduction | Example | tidy method | augment method | glance method | Additional examples

Model and notation | Estimation | Simulation | Appendix: Score equations