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Choosing a new CD4 technology: can statistical method comparison tools influence the decision?

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dc.contributor.author Scott, L. E. en_US
dc.contributor.author Kestens, L. en_US
dc.contributor.author Pattanapanyasat, K. en_US
dc.contributor.author Sukapirom, K. en_US
dc.contributor.author Stevens, W. S. en_US
dc.date.accessioned 2017-12-18T12:55:50Z
dc.date.available 2017-12-18T12:55:50Z
dc.date.issued 2017 en_US
dc.identifier.issn 1552-4949 en_US
dc.identifier.doi http://dx.doi.org/10.1002/cyto.b.21522 en_US
dc.identifier.other http://lib.itg.be/pdf/itg/2017/2017cbcc0001.pdf en_US
dc.identifier.other ITG-B2A; DBM; U-IMMUN; JIF; DOI; PDF; Abstract; DSPACE64 en_US
dc.identifier.uri http://hdl.handle.net/10390/9631
dc.description.abstract BACKGROUND: Method comparison tools are used to determine the accuracy, precision, agreement and clinical relevance of a new or improved technology versus a reference technology. Guidelines for the most appropriate method comparison tools as well as their acceptable limits are lacking and not standardised for CD4 counting technologies. METHODS: Different method comparison tools were applied to a previously published CD4 data set (n= 150 data pairs) evaluating five different CD4 counting technologies (TruCOUNT, Dual Platform, FACSCount, Easy CD4, CyFlow) on a single specimen. Bland-Altman, percentage similarity, percent difference, concordance correlation, sensitivity, specificity and misclassification method comparison tools were applied as well as visualization of agreement with Passing Bablock and Bland-Altman scatter plots. RESULTS: The FACSCount (median CD4 = 245cells/microl) was considered the reference for method comparison. An algorithm was developed using best practices of the most applicable method comparison tools, and together with a modified heat map was found useful for method comparison of CD4 qualitative and quantitative results. The algorithm applied the concordance correlation for overall accuracy and precision, then standard deviation of the absolute bias and percentage similarity coefficient of variation to identify agreement, and lastly sensitivity and misclassification rates for clinical relevance. CONCLUSION: Combining method comparison tools is more useful in evaluating CD4 technologies compared to a reference CD4. This algorithm should be further validated using CD4 external quality assessment data and studies with larger sample sizes. This article is protected by copyright. All rights reserved. en_US
dc.language English en_US
dc.relation.uri http://www.ncbi.nlm.nih.gov/pubmed/28296063 en_US
dc.subject Molecular en_US
dc.subject Medicine en_US
dc.subject Universities en_US
dc.subject Health en_US
dc.subject Science en_US
dc.subject School en_US
dc.subject Pathology en_US
dc.subject Antwerp en_US
dc.subject Belgium en_US
dc.subject Laboratory en_US
dc.subject Laboratories en_US
dc.subject Immunology en_US
dc.subject Tropical en_US
dc.subject Tropical medicine en_US
dc.subject Flow cytometry en_US
dc.subject Cytometry en_US
dc.subject Research en_US
dc.subject Development en_US
dc.subject Hospital en_US
dc.subject Thailand en_US
dc.subject National en_US
dc.subject National health en_US
dc.subject South Africa en_US
dc.subject Africa en_US
dc.title Choosing a new CD4 technology: can statistical method comparison tools influence the decision? en_US
dc.type Article-P en_US
dc.citation.issue 6 en_US
dc.citation.jtitle Cytometry. Part B, Clinical cytometry en_US
dc.citation.volume 92 en_US
dc.citation.pages 465-475 en_US
dc.citation.abbreviation Cytometry B Clin Cytom en_US


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