Stepped wedge trials (SWT) is a special type of crossover cluster randomized trials (CRT) in which clusters are randomized to initiate treatment at different points in time. This design is proposed for a future trial that aims to study the effectiveness of a population health management (PHM) intervention based on Electronic Health Record (EHR) among patients with CKD. This study will collect multi-level data with longitudinal kidney function measurements from patients nested within practices. There are two challenges in powering this trial: (1) existing literature to power SWTs focus on cross-sectional design and single level of clustering; and (2) patients enrolled in the EHR-PHM trial may experience delayed treatment initiation in which they receive treatment later than expected. The effect of delayed treatment initiation to power has not been discussed in the current literature.
The goal of this thesis is to develop a simulation-based method for power and sample size analysis for SWTs with longitudinal outcomes and delayed treatment initiation. We assumed random effects mixed models to account for correlation introduced by multiple levels of clustering. Simulation parameters are acquired from preliminary EHR data and verified by nephrologists. We determined the power and sample size requirements with varying levels of delayed treatment initiation.
We have found that delayed treatment initiation results in slight loss of power. The impact of varying levels of subject delay for a fixed time delay is similar to the impact of varying levels of time delay for a fixed subject delay. Simulation-based power calculation is a flexible and practical tool in designing SWT with longitudinal measurements.
Public health significance: In clinical trials, the simulation-based power calculation method provides a practical and flexible approach for power calculation and sample size determination in designing SWTs with longitudinal outcomes while incorporating the effect of delayed treatment initiation. This method will be useful in the design of the EHR-PHM trial which could potentially improve care for and outcomes of high risk CKD patients
