Abstract
BackgroundRandomized controlled trials (RCTs) of behavior-based interventions are particularly vulnerable to post-randomization changes between study arms. We assess the impact of such a change in a large, multicenter study of universal contact precautions to prevent infection transmission in intensive care units.MethodsWe construct a stochastic mathematical model of methicillin-resistant Staphylococcus aureus (MRSA) acquisition in a simulated 18-bed intensive care unit (ICU). Using parameters from a recent study of contact precautions that reported a post-randomization change in contact rates, with fewer visits observed in the treatment arm, we explore the impact of several possible interpretations of this change on MRSA acquisition rates.ResultsScenarios where contact precautions resulted in less patient visitation resulted in a mean decrease in MRSA acquisition rate of 37%, accounting for much of the effect reported in the trial.ConclusionsBehavior changes that impact the contact rate have the potential to drastically alter the results of RCTs in infection control settings. Careful monitoring for these changes, and an assessment of which changes will likely have the greatest impact on the study before the study begins are both recommended.
Highlights
Randomized controlled trials (RCTs) of behavior-based interventions are vulnerable to post-randomization changes between study arms
This study found that the use of a universal gowning and gloving policy reduced methicillin-resistant Staphylococcus aureus (MRSA) acquisitions by 40.2%, reflecting 2.98 fewer acquisitions per 1000 patient days (95% Confidence Interval (CI): 5.58 to 0.38)
If the reduction in Healthcare worker (HCW) visits reported in Harris et al represents a reduction in patient contact, the observed reduction in MRSA acquisitions can be almost entirely explained by the post-randomization changes in HCW visitation rates and hand hygiene
Summary
Randomized controlled trials (RCTs) of behavior-based interventions are vulnerable to post-randomization changes between study arms. The process of randomization removes any differences between the treatment and control arms of the trial, yielding a theoretically unbiased estimate of a causal effect. In multi-center trials of hospital-level policy interventions these post-randomization changes can arise from differences in the patients between the two arms, but differences in the behavior of healthcare personnel at different sites [1]. If these differences in behavior result in differential quality of care between the two study arms, bias can arise. A failure to detect and quantify these effects can potentially lead to errant clinical practice, hospital policy, and professional guidelines
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