Addressing Interval Censoring in the Stepped Wedge Design of Cluster-Randomized Clinical Trials

• Activity Number: 469
• Type: Contributed
• Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Tanya Granston*+ and James Hughes
• Companies: and University of Washington
• Keywords: mixed models ; interval censoring ; EM Algorithm ; trial design

Abstract

The stepped wedge design of cluster-randomized trials has been growing in popularity and becomes increasingly relevant due to the need to efficiently evaluate the rollout of interventions that are individually efficacious in a community setting. The design is especially useful in resource limited settings where it is usually not feasible to introduce interventions all at once and where there are substantial clusters/communities/groups to provide answers regarding intervention effects. However, there remain limitations and outstanding issues to this design, which could potentially limit its usefulness where it's most needed. One such limitation is the lack of analytic methods to address interval censoring of time-to-event endpoints in the context of the stepped wedge design. We propose an EM Algorithm approach with cluster effect and true failure times as joint latent variables to address this issue in discrete time and evaluate the precision, consistency, and asymptotic normality of the hazard ratio estimator in simulations. Additionally, we outline the bias and power using this approach.