Sample sizes for estimating the sensitivity of a monitoring system that generates repeated binary outcomes with autocorrelation

Sample size formulas are provided to determine how many events and how many patient care units are needed to estimate the sensitivity of a monitoring system. The monitoring systems we consider generate time series binary data that are autocorrelated and clustered by patient care units. Our applicati...

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Bibliographic Details
Published in:Statistical methods in medical research 2023-12, Vol.32 (12), p.2347-2364
Main Authors: Parker, Albert E, Arbogast, James W
Format: Article
Language:English
Subjects:
Autocorrelation
Binary data
Cluster analysis
Confidence intervals
Health care
Health facilities
Hygiene
Medical personnel
Monitoring
Patients
Regression analysis
Regression models
Sample size
Sensitivity
Statistical analysis
Time series
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Summary:Sample size formulas are provided to determine how many events and how many patient care units are needed to estimate the sensitivity of a monitoring system. The monitoring systems we consider generate time series binary data that are autocorrelated and clustered by patient care units. Our application of interest is an automated hand hygiene monitoring system that assesses whether healthcare workers perform hand hygiene when they should. We apply an autoregressive order 1 mixed effects logistic regression model to determine sample sizes that allow the sensitivity of the monitoring system to be estimated at a specified confidence level and margin of error. This model overcomes a major limitation of simpler approaches that fail to provide confidence intervals with the specified levels of confidence when the sensitivity of the monitoring system is above 90%.
ISSN:0962-2802
1477-0334
DOI:10.1177/09622802231208058