Test result-based sampling: an efficient design for estimating the accuracy of patient safety indicators.
Details
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State: Public
Version: author
Serval ID
serval:BIB_C24C6B9B03DD
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Test result-based sampling: an efficient design for estimating the accuracy of patient safety indicators.
Journal
Medical Decision Making
Working group(s)
International Methodology Consortium for Coded Health Information (IMECCHI)
Contributor(s)
Burnand B., Colin C., Couris C., De Coster C., Drosler S., Finlayson A., Fushimi K., Gao M., Ghali W., Halfon P., Hemmelgarn B., Humphries K., Januel JM., Johansen H., Lix L., Luthi JC., Ma J., Quan H., Romano P., Roos L., Shrive F., Sundararajan V., Touzet S., Tu J., Webster G.
ISSN
1552-681X (Electronic)
ISSN-L
0272-989X
Publication state
Published
Issued date
2012
Peer-reviewed
Oui
Volume
32
Number
1
Pages
E1-12
Language
english
Notes
Publication types: Journal Article
Publication Status: ppublish
Publication Status: ppublish
Abstract
OBJECTIVE: Accuracy studies of Patient Safety Indicators (PSIs) are critical but limited by the large samples required due to low occurrence of most events. We tested a sampling design based on test results (verification-biased sampling [VBS]) that minimizes the number of subjects to be verified.
METHODS: We considered 3 real PSIs, whose rates were calculated using 3 years of discharge data from a university hospital and a hypothetical screen of very rare events. Sample size estimates, based on the expected sensitivity and precision, were compared across 4 study designs: random and VBS, with and without constraints on the size of the population to be screened.
RESULTS: Over sensitivities ranging from 0.3 to 0.7 and PSI prevalence levels ranging from 0.02 to 0.2, the optimal VBS strategy makes it possible to reduce sample size by up to 60% in comparison with simple random sampling. For PSI prevalence levels below 1%, the minimal sample size required was still over 5000.
CONCLUSIONS: Verification-biased sampling permits substantial savings in the required sample size for PSI validation studies. However, sample sizes still need to be very large for many of the rarer PSIs.
METHODS: We considered 3 real PSIs, whose rates were calculated using 3 years of discharge data from a university hospital and a hypothetical screen of very rare events. Sample size estimates, based on the expected sensitivity and precision, were compared across 4 study designs: random and VBS, with and without constraints on the size of the population to be screened.
RESULTS: Over sensitivities ranging from 0.3 to 0.7 and PSI prevalence levels ranging from 0.02 to 0.2, the optimal VBS strategy makes it possible to reduce sample size by up to 60% in comparison with simple random sampling. For PSI prevalence levels below 1%, the minimal sample size required was still over 5000.
CONCLUSIONS: Verification-biased sampling permits substantial savings in the required sample size for PSI validation studies. However, sample sizes still need to be very large for many of the rarer PSIs.
Keywords
Adult, Algorithms, Confidence Intervals, Hospitals, University/standards, Humans, Inpatients, Medical Errors/prevention & control, Middle Aged, Patient Safety/statistics & numerical data, Quality Indicators, Health Care/standards, Sensitivity and Specificity
Pubmed
Web of science
Create date
11/11/2011 9:29
Last modification date
20/08/2019 16:37
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