Bayesian analysis

In recent posts, I introduced a Bayesian approach to proportional hazards modeling and then extended it to incorporate a penalized spline. (There was also a third post on handling ties when multiple individuals share the same event time.) This post describes another extension: a random effect to account for clustering in a cluster randomized trial. With this in place, I’ll be ready to tackle the final step—building a model for analyzing a stepped-wedge cluster-randomized trial that incorporates both splines and site-specific random effects. [Read More]

Over my past few posts, I’ve been progressively building towards a Bayesian model for a stepped-wedge cluster randomized trial with a time-to-event outcome, where time will be modeled using a spline function. I started with a simple Cox proportional hazards model for a traditional RCT, ignoring time as a factor. In the next post, I introduced a nonlinear time effect. For the third post—one I initially thought was ready to publish—I extended the model to a cluster randomized trial without explicitly incorporating time. [Read More]

In my previous post, I outlined a Bayesian approach to proportional hazards modeling. This post serves as an addendum, providing code to incorporate a spline to model a time-varying hazard ratio non linearly. In a second addendum to come I will present a separate model with a site-specific random effect, essential for a cluster-randomized trial. These components lay the groundwork for analyzing a stepped-wedge cluster-randomized trial, where both splines and site-specific random effects will be integrated into a single model. [Read More]