Negative Spillover: A Potential Source of Bias in Pragmatic Clinical Trials

TL;DR

This paper discusses how negative spillover in pragmatic clinical trials can bias results by affecting resource allocation between groups, potentially leading to overestimated treatment effects.

Contribution

It identifies causes of negative spillover, illustrates its impact on trial outcomes, and proposes methods to detect and prevent this bias in clinical research.

Findings

Negative spillover can cause overestimation of treatment effects.

Examples include patient navigation, AI alarms, and labor induction.

Strategies for detecting and avoiding spillover bias are suggested.

Abstract

Pragmatic clinical trials evaluate the effectiveness of health interventions in real-world settings. Negative spillover can arise in a pragmatic trial if the study intervention affects how scarce resources are allocated between patients in the intervention and comparison groups. This can harm patients assigned to the control group and lead to overestimation of treatment effect. While this type of negative spillover is often addressed in trials of social welfare and public health interventions, there is little recognition of this source of bias in the medical literature. In this article, I examine what causes negative spillover and how it may have led clinical trial investigators to overestimate the effect of patient navigation, AI-based physiological alarms, and elective induction of labor. I also suggest ways to detect negative spillover and design trials that avoid this potential source of bias.