title: Choosing a new CD4 technology: Can statistical method comparison tools influence the decision?
abstract: 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 standardized for CD4 counting technologies.
  
  
  
  Different method comparison tools were applied to a previously published CD4 dataset (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.
  
  
  
  The FACSCount (median CD4 = 245 cells/µl) 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.
  
  
  
  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. © 2017 International Clinical Cytometry Society.
authors.affiliation: Department of Molecular Medicine and Haematology, Faculty of Health Science, School of Pathology, University of the Witwatersrand, Johannesburg, South Africa.
authors.forename: Lesley E
authors.initials: LE
authors.lastname: Scott
authors.affiliation: Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium and Laboratory of Immunology, Department of Biomedical Sciences, Institute of Tropical Medicine, Antwerp, Belgium.
authors.forename: Luc
authors.initials: L
authors.lastname: Kestens
authors.affiliation: Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; Center for Emerging and Neglected Infectious Diseases, Mahidol University, Nakhon Pathom, Thailand.
authors.forename: Kovit
authors.initials: K
authors.lastname: Pattanapanyasat
authors.affiliation: Department of Research and Development, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand; Center for Emerging and Neglected Infectious Diseases, Mahidol University, Nakhon Pathom, Thailand.
authors.forename: Kasma
authors.initials: K
authors.lastname: Sukapirom
authors.affiliation: Department of Molecular Medicine and Haematology, Faculty of Health Science, School of Pathology, University of the Witwatersrand, Johannesburg, South Africa.|Department of Molecular Medicine and Haematology, The National Health Laboratory Service, Johannesburg, South Africa.
authors.forename: Wendy S
authors.initials: WS
authors.lastname: Stevens
mesh_terms: D000465:Algorithms; D000704:Analysis of Variance; D057205:Automation, Laboratory / Q000295:instrumentation / Q000592:standards*; D018791:CD4 Lymphocyte Count / Q000295:instrumentation / Q000592:standards*; D015496:CD4-Positive T-Lymphocytes / Q000276:immunology*; D005434:Flow Cytometry / Q000295:instrumentation / Q000592:standards*; D006801:Humans; D016130:Immunophenotyping / Q000295:instrumentation / Q000379:methods / Q000592:standards*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Bland-Altman; CD4 technology; concordance correlation; method comparison; misclassification; percentage similarity; statistical algorithm