Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects

TL;DR

This paper introduces assumption-lean methods for analyzing cluster randomized trials in infectious diseases, focusing on intent-to-treat and network effects without relying on strong parametric assumptions.

Contribution

It develops new non-parametric approaches for estimating ITT and network effects, including a bound-based method that improves with better classification algorithms.

Findings

Reanalysis of a face mask and sanitizer trial in Hong Kong.

Proposed bounds become sharper with improved classification.

Methods applicable for partial identification with instrumental variables.

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, usually mixed-effect models to account for the clustering structure, and focuses on the overall intent-to-treat (ITT) effect to evaluate effectiveness. The paper 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 and the 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, classification algorithms, 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.