Simulating trial data | Simulating external control data | Merging trial and external control data | Propensity scores overlap | Matching Methods | PSML | PSMR | GM | GMW | OM | Weighting Methods | GBM | EB | IPW | Balance Plots for Matching Methods | Balance Plots for Weighting Methods | Selection of matching/weighting methods | References

Before you start | Creating an analysis object | 1. data_matrix | Required elements | Time-to-event | Binary endpoints | Covariates | Example data | Creating a data matrix with create_data_matrix() | psborrow2 example matrix | 2. outcome | 3. borrowing | 3. treatment | Application | Sampling from an analysis object | Summarizing results | Using bayesplot | Using posterior

Hazard function | Cox proportional hazards model | Baseline hazard function | Likelihood | Power prior model | Commensurate prior model | Conducting Analysis | Example data | Exponential distribution with constant hazard with gamma prior distribution (Using psborrow2 R package) | Time-to-event analysis with Weibull distribution and proportional hazards parametrization | Power prior with piecewise exponential distribution (not implemented in psborrow2 R package) | Commensurate prior with piecewise exponential distribution (not implemented in psborrow2 R package) | Exponential distribution (constant hazard) and no borrowing: Using psborrow2 R package | Exponential distribution (constant hazard) and full borrowing: Using psborrow2 R package | Cox proportional hazards model using frequentist approach (No borrowing) | Cox proportional hazards model using frequentist approach (Full borrowing from external control) | Summarizing all the results together | References

Bringing your own simulated data | data_matrix_list | data_list | guide | effect, drift, and index | outcome | borrowing | covariate | treatment | create_simulation_obj() | mcmc_sample() | MSE | Type I error | Power | EHSS | Optimal Accuracy Design | References

Alternative PS Weights with WeightIt | Matching with MatchIt | Combined Weighting and Dynamic Borrowing | Fixed Weights | Reference Models | Comparison of Results | References

Introduction | Logistic regression | glm | BayesPPD | psborrow2 | Results | Exponential models

Generating Baseline Data | Generating Survival Data | Enrollment | Drop Out | Clinical Cut Off | Running a simulation | Using fixed external data | Combining simulations

Purpose | Explore example data | {psborrow2} contains an example matrix | Load as data.frame for some functions | Look at distribution of arms | Naive internal comparisons | Kaplan-Meier curves | Cox model | Hybrid control analysis | A note on prior distributions | Outcome objects | Create an exponential survival distribution Outcome object | Borrowing object | Treatment objects | Analysis objects | MCMC sampling | Interpret results | Control arm imbalances | Propensity score analysis with BDB

Specifying prior distributions | Types of prior distributions | Visualizing prior distributions

0. Analysis workflow & supported model types | 1. ER model development | 2. Simulation from developed ER model | 3. Plot simulated ER curve | Acknowledgement

Introduction | 1. Apply Bayesian dynamic borrowing methods | 2. Conduct simulation studies of Bayesian dynamic borrowing methods | 3. Generate data for simulation studies | Additional articles | Installing cmdstanr | References