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Activity Number: 408 - SPAAC Poster Competition
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Scientific and Public Affairs Advisory Committee
Abstract #330175
Title: Estimation and Inference for Cluster-Randomized Test-Negative Design Trials
Author(s): Suzanne M. Dufault* and Nicholas P. Jewell
Companies: University of California, Berkeley and University of California, Berkeley
Keywords: cluster-randomized; test-negative; dengue; randomized intervention trial
Abstract:

Motivated by a current vector control trial designed to reduce dengue incidence, we propose the Cluster-Randomized Test-Negative Design (CR-TND) to assess a binary endpoint exploiting the test-negative design. We propose two ways of estimating the Relative Risk (RR); (i) a modified two-sample t-statistic with permutation-based inference, and (ii) an Odds Ratio (OR) approach that appropriately controls for clustering.

In simulation studies of 24 clusters assigned to parallel treatment arms with RR = 1, 0.6, 0.5, 0.4, 0.3, we compared the performance of the proposed methods to GEE estimation with robust inference and to random effects (RE) regression with random intercepts for each cluster. GEE performed poorly across all metrics. The desirable Type I error rate was produced by our t-test method. For each RR, all methods (except GEE) displayed 92% confidence interval coverage or higher, with the t-test method consistently above 94%. Further, the average estimated SD for each method (except GEE) was within 2% of the truth.

We briefly compare the described approaches to one that uses information only on cases through comparison across the two randomized arms.


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

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