Bayesian Hierarchical Penalized Spline Models in Stepped Wedge Cluster Randomized Trials

Music City Center 

Traditional frequentist methods may not provide adequate coverage of an intervention's true effect using confidence intervals in the context of stepped wedge cluster randomized trials (SWCRTs), whereas Bayesian approaches remain underexplored in SWCRTs. To bridge this gap, we propose two innovative Bayesian hierarchical penalized spline models. Our first model focuses on immediate intervention effects. We then extend it to account for time-varying intervention effects. Through extensive simulations and the real-world application, we demonstrate the robustness of our proposed Bayesian models. Notably, the Bayesian immediate effect model consistently achieves the nominal coverage probability, providing more reliable interval estimations while maintaining high estimation accuracy. Furthermore, the Bayesian time-varying effect model represents a significant advancement over the existing Bayesian monotone effect curve models, offering improved accuracy and reliability in estimation, while also achieving higher coverage probability than alternative frequentist methods. To the best of our knowledge, this marks the first development of Bayesian hierarchical spline modeling for SWCRTs.

Cluster randomized trial

Bayesian hierarchical models

Penalized spline

Time-varying treatment effect

Stepped wedge 

International Society for Bayesian Analysis (ISBA)