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Visualizing and diagnosing spillover within randomized concurrent controlled trials through the application of
James C Hurley1,2,3
1Melbourne Medical School, University of Melbourne, Ballarat, Australia. jamesh@gh.org.au.
BMC Medical Research Methodology
|August 16, 2024
Summary
Spillover effects in randomized trials can invalidate results. New methods analyzing arm-based data reveal harmful spillover in antimicrobial interventions for pneumonia prevention, contrary to standard contrast-based analyses.
Area of Science:
- Epidemiology
- Biostatistics
- Infectious Disease Prevention
Background:
- The Stable Unit Treatment Variable Assumption (SUTVA) is crucial for causal inference in randomized controlled trials, but spillover effects can violate it.
- Spillover, particularly in infection prevention, can influence herd immunity and is often embedded within overall effect size estimates.
- Herd effects manifest as increased dispersion in infection incidence rates within control groups.
Purpose of the Study:
- To explore methods for visualizing and diagnosing spillover effects in randomized controlled trials.
- To investigate spillover in antimicrobial versus non-antimicrobial interventions for preventing pneumonia in ICU patients.
Main Methods:
- Utilized data from 190 randomized concurrent controlled trials (RCCTs) across 13 Cochrane reviews.
- Employed arm-based analytical techniques, including benchmarking incidence rates, comparing dispersion between control and intervention groups, and using summary receiver operator characteristic (SROC) plots.
- These methods were used to diagnose spillover in infection prevention RCCTs.
Main Results:
- Arm-based techniques identified increased dispersion indicative of spillover, which contrast-based techniques do not reveal.
- Analysis of pneumonia prevention interventions in ICU patients showed harmful spillover effects to concurrent control group patients.
- The study found increased dispersion in control group infection incidence rates.
Conclusions:
- Arm-based and contrast-based techniques yield contradictory inferences from aggregated RCCTs of antimicrobial interventions.
- The relationship between underlying control group risk and effect size is inverted when using arm-based methods.
- Harmful spillover effects necessitate re-evaluation of standard analytical approaches in certain RCCT contexts.
Keywords:
Arms-basedCaterpillar plotsContrast-basedDiagnostic test assessmentHeterogeneityInfection preventionIntensive careRandomized concurrent controlled trialsSROC plotsSpilloverMore Related Videos
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