Laboratory Statistics

Dr. Kallner studied general chemistry at the University of Stockholm and organic chemistry at the International Union of Pure and Applied Chemistry (IUPAC) Elections Royal Institute of Technology in Stockholm before graduating with a PhD in biochemistry from the Karolinska Institute in 1967. He later earned his MD at the same university and became Associate Professor of Clinical Chemistry at the Karolinska Institute. He has held positions in county, regional, and university hospitals. Although he retired from Karolinska University Hospital in 2005, Dr. Kallner retains professional assignments in the laboratory and international organizations. He has given more than 250 invited lectures and has contributed to more than 180 publications. Dr. Kallner has held numerous memberships and leadership roles on numerous international committees, including the International Organization for Standardization (ISO), the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC), and the IUPAC. Dr. Kallner has participated in the development of several CLSI Evaluation Protocols, and is currently the chair holder of the Subcommittee on Expression of Measurement Uncertainty in Laboratory Medicine (C51) and an active member of the CLSI Area Committee on Evaluation Protocols. He has chaired and participated in the development of a standard in metrology in ISO, CEN, and CLSI.Dr. Kallner’s scientific work has spanned a wide field ranging from organic synthesis and metabolism of cholesterol to epidemiological and metabolic studies of vitamin D. An interest in quality management and development of routines for quality assessment in the laboratory required studies in programming and statistics. Eventually, Dr. Kallner recognized the need for a compendium of useful statistical procedures and formulas that could easily be used in programming and understanding of statistical procedure.

1. Basics2. Distributions of Data3. Permutations, Combinations, Probability, Binominal and Poisson Distributions4. Robust Estimators5. Uncertainty6. Difference Between Results7. Non-Parametric Comparisons8. Analysis of Variance9. Analysis of Variance Components10. Regression11. Correlation and Covariance12. Comparing Quantities13. Performance Characteristics14. Interpretation of the Elements of the ROC Analysis15. Estimation of Minimal Sample Size (Power Analysis)16. Agreement Between Categorical Assessments (Kappa (?)-Statistics) Some Metrological Concepts17. Metrology, Accuracy, Trueness and Precision18. Uncertainty Concept and Uncertainty Budget19. Miscellanea