Negative spillover due to constraints on care delivery: a potential source of bias in pragmatic clinical trials

Trials. 2024 Dec 18;25(1):833. doi: 10.1186/s13063-024-08675-9.

Abstract

Background: 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 across patients in the intervention and comparison groups.

Main body: Negative spillover can lead to overestimation of treatment effect and harm to patients assigned to usual care in trials of diverse health interventions. While this type of spillover has been 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 commentary, 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. Trials discussed here are a convenience sample and not the result of a systematic review. I also suggest ways to detect negative spillover and design trials that avoid this potential source of bias.

Conclusion: As new clinical practices and technologies that affect care delivery are considered for widespread adoption, well-designed trials are needed to provide valid evidence on their risks and benefits. Understanding all sources of bias that could affect these trials, including negative spillover, is a critical part of this effort. Future guidance on clinical trial design should consider addressing this form of spillover, just as current guidance often discusses bias due to lack of blinding, differential attrition, or contamination.

Keywords: Bias; Clinical trial; Health system capacity; Patient safety; Resource utilization; Study design.

Publication types

  • Letter

MeSH terms

  • Bias*
  • Delivery of Health Care / standards
  • Humans
  • Pragmatic Clinical Trials as Topic*
  • Research Design*
  • Treatment Outcome