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Activity Number: 655 - Improving Power and Generalizability in Causal Effect Estimation Using Multicenter and Network Designs
Type: Topic Contributed
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #330521 Presentation
Title: Competing Effects of Indirect Protection and Clustering on the Power of a Cluster-Randomized Controlled Vaccine Trial
Author(s): Matthew Hitchings*
Companies: Harvard School of Public Health
Keywords: vaccine; modeling; trial

While cluster-randomized trials (cRCTs) are less efficient than individually randomized trials (iRCT), a cRCT's ability to measure direct and indirect vaccine effects may mitigate the loss of efficiency due to clustering. Within cRCTs, the number and size of clusters affects three determinants of power: the effect size being measured, disease incidence, and intra-cluster correlation. We simulate trials in a collection of small communities to assess how indirect protection and clustering affect the power of cRCTs and iRCTs during an emerging epidemic. Across diverse parameters, we find that within the same trial population, cRCTs are never more powerful than iRCTs, though the difference can be small. We also identify two effects that attenuate the loss of cRCT power traditionally associated with increased cluster size. First, if enrollment of fewer, larger clusters is performed to achieve higher vaccine coverage within vaccinated communities, this increases the effect to be measured and, consequently, power. Second, the greater rate of imported transmission in larger communities may increase the attack rate and mitigate loss of power relative to a trial in many, smaller communities.

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

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