Background | Discrete Time Survival Analysis | Hazard Rates and Cox Proportional Hazards Models | Expanding Data to Person-Time Format | Formatting Data for the brea Package | Bayesian Inference via MCMC Simulation | Prior and Posterior Distributions | Inference via MCMC Sampling from the Posterior Distribution | Detailed Example of a Simple Cox Model | Classical Partial Likelihood Inference using coxph() | Discrete Time Survival Data Preparation | Frequentist Logistic Regression Inference using glm() | Polynomial Time Effect | Step Function Time Effect | Bayesian Inference using brea_mcmc() | Setting Up an MCMC Run | Extracting and Examining Results | Assessing MCMC Performance and Adjusting MCMC Run Length S | Better Step Function Approximation using the GMRF Prior Type | Inferences | Comparison with Other Approaches | References

Background | Modeling Nonlinear Covariate Effects in the Discrete Cox Model | The Discrete Time Cox Proportional Hazards Model | Additive Modeling of Nonlinear Covariate Effects | Modeling Nonlinear Effects Using Step Functions | Bayesian Smoothing of Step Heights Using GMRF's | Metropolis-Hastings Inference for Parameters with GMRF Priors | Example Implementation of a Smooth Baseline Hazard in a Cox Model | Data Setup | Model Fitting | Posterior Inference | Comparing Different Step Widths | MCMC Performance Assessment and Comparison | References