Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 315 - Biometrics Section Byar Award Student Paper Session I
Type: Topic-Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Biometrics Section
Abstract #317301
Title: Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects
Author(s): Chan Park* and Hyunseung Kang
Companies: University of Wisconsin-Madison and University of Wisconsin-Madison
Keywords: Cluster randomized trials; Effect heterogeneity; Infectious diseases; Intent-to-treat effect; Noncompliance; Bound
Abstract:

Cluster randomized trials (CRTs) are a popular design to study the effect of interventions in infectious disease settings. However, standard analysis of CRTs primarily relies on strong parametric methods to account for the clustering structure, and focus on the overall intent-to-treat (ITT) effect to evaluate effectiveness. We presents two assumption-lean methods to analyze two types of effects in CRTs, ITT effects and network effects among well-known compliance groups. For the ITT effects, we study the overall/heterogeneous ITT effects among the observed covariates where we do not impose parametric models or asymptotic restrictions on cluster size. For the network effects among compliance groups, we propose a new bound-based method that uses pre-treatment covariates, classifiers, and a linear program to obtain sharp bounds. A key feature of our method is that the bounds can become narrower as the classification algorithm improves and the method may also be useful for studies of partial identification with instrumental variables. We conclude by reanalyzing a CRT studying the effect of face masks and hand sanitizers on transmission of 2008 interpandemic influenza in Hong Kong.


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

Back to the full JSM 2021 program