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Activity Number: 29 - Biometrics Section Byar Award Student Paper Session I
Type: Topic Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 11:50 AM
Sponsor: Biometrics Section
Abstract #309533
Title: Randomization-Based Confidence Intervals for Cluster Randomized Trials
Author(s): Dustin Rabideau* and Rui Wang
Companies: Harvard University and Harvard University
Keywords: Cluster randomized trial; Confidence interval; Correlated data; Interval-censored; Permutation test; Randomization-based inference

In a cluster randomized trial (CRT), groups of people are randomly assigned to different interventions. Existing parametric and semiparametric methods for CRTs rely on distributional assumptions or a large number of clusters to maintain nominal confidence interval (CI) coverage. Randomization-based inference is an alternative approach that is distribution-free and does not require a large number of clusters to be valid. Although it is well-known that a CI can be obtained by inverting a randomization test, this requires testing a non-zero null hypothesis, which is challenging with non-continuous and survival outcomes. In this paper, we propose a general method for randomization-based CIs using individual-level data from a CRT. This approach accommodates various outcome types, can account for design features such as matching or stratification, and employs a computationally efficient algorithm. We evaluate this method's performance through simulations and apply it to the Botswana Combination Prevention Project, a large HIV prevention trial with an interval-censored time-to-event outcome.

Authors who are presenting talks have a * after their name.

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