Abstract
An important step in designing, executing, and evaluating cluster-randomized trials (CRTs) is understanding the correlation and thus nonindependence that exists among individuals in a cluster. In hospital epidemiology, there is a shortage of CRTs that have published their intraclass correlation coefficient or coefficient of variation (CV), making prospective sample size calculations difficult for investigators. To estimate the number of hospitals needed to power parallel CRTs of interventions to reduce health care-associated infection outcomes and to demonstrate how different parameters such as CV and expected effect size are associated with the sample size estimates in practice. This longitudinal cohort study estimated parameters for sample size calculations using national rates developed by the Centers for Disease Control and Prevention for methicillin-resistant Staphylococcus aureus (MRSA) bacteremia, central-line-associated bloodstream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and Clostridium difficile infections (CDI) from 2016. For MRSA and vancomycin-resistant enterococci (VRE) acquisition, outcomes were estimated using data from 2012 from the Benefits of Universal Glove and Gown study. Data were collected from June 2017 through September 2018 and analyzed from September 2018 through January 2019. Calculated number of clusters needed for adequate power to detect an intervention effect using a 2-group parallel CRT. To study an intervention with a 30% decrease in daily rates, 73 total clusters were needed (37 in the intervention group and 36 in the control group) for MRSA bacteremia, 82 for CAUTI, 60 for CLABSI, and 31 for CDI. If a 10% decrease in rates was expected, 768 clusters were needed for MRSA bacteremia, 875 for CAUTI, 631 for CLABSI, and 329 for CDI. For MRSA or VRE acquisition, 50 or 40 total clusters, respectively, were required to observe a 30% decrease, whereas 540 or 426 clusters, respectively, were required to detect a 10% decrease. This study suggests that large sample sizes are needed to appropriately power parallel CRTs targeting infection prevention outcomes. Sample sizes are most associated with expected effect size and CV of hospital rates.
Highlights
IntroductionHospital or health care epidemiology is the branch of epidemiology that focuses on the understanding, prevention, and control of health care–associated infections (HAIs), which are acquired in a health care setting.[1]
If a 10% decrease in rates was expected, 768 clusters were needed for methicillin-resistant Staphylococcus aureus (MRSA) bacteremia, 875 for catheter-associated urinary tract infections (CAUTI), 631 for central-line– associated bloodstream infections (CLABSI), and 329 for Clostridium difficile infections (CDI)
This study suggests that large sample sizes are needed to appropriately power parallel cluster-randomized trials (CRTs) targeting infection prevention outcomes
Summary
Hospital or health care epidemiology is the branch of epidemiology that focuses on the understanding, prevention, and control of health care–associated infections (HAIs), which are acquired in a health care setting.[1]. In the field of hospital epidemiology, the cluster-randomized trial (CRT) design is frequently used. Intact social units or clusters of individuals such as wards, intensive care units (ICUs), or hospitals rather than independent individuals are randomized to intervention groups.[3,4] This is often the design of choice because randomization cannot occur at the individual patient level owing to ethical issues or group-level confounding variables, known as treatment group contamination.[4,5] in practice, this design has several advantages compared with individually randomized trials, such as increased administrative efficiency, reduced risk of treatment group contamination, and likely improvement of participant compliance.[4]
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