Statistical Topics in the Laboratory Sciences

  • Curtis A. Parvin
Part of the Methods in Molecular Biology™ book series (MIMB, volume 404)


This chapter concerns statistical concepts and procedures that are applicable to diagnostic testing performed in the clinical laboratory. Three important laboratory issues are addressed: the estimation of analytical imprecision, the design of an effective laboratory quality control strategy, and the establishment of population reference ranges. These three topics were selected because each demonstrates a valuable statistical principle. Estimation of analytical imprecision highlights the important role of study design. Evaluating laboratory quality control strategies emphasizes the importance of choosing appropriate statistical models. The estimation of population reference ranges demonstrates that there can be many different approaches to developing good statistical estimators.

Key Words

Quality control reference limits variance components 


  1. 1.
    Neter, J., Kutner, M. H., Wasserman, W., and Nachtsheim, C. J. (1996) Applied Linear Statistical Models, 4th ed. Homewood, Irwin.Google Scholar
  2. 2.
    Clinical and Laboratory Standards Institute. (2004) Evaluation of Precision Performance of Quantitative Measurement Methods; Approved GuidelineSecond Edition. EP5-A2. Villanova, Clinical and Laboratory Standards Institute.Google Scholar
  3. 3.
    Littell, R. C., Milliken, G. A., Stroup, W. W., Wolfinger, R. D., and Schabenberger, O. (2006) SAS for Mixed Models, 2nd ed. Cary, SAS Institute.Google Scholar
  4. 4.
    Satterthwaite, F. E. (1946) An approximate distribution of estimates of variance components. Biometrics 2, 110–114.PubMedCrossRefGoogle Scholar
  5. 5.
    Clinical and Laboratory Standard Institute. (1999) Statistical Quality Control for Quantitative Measurements: Principles and Definitions; Approved GuidelineSecond Edition. C24-A2. Villanova, Clinical and Laboratory Standards Institute.Google Scholar
  6. 6.
    Westgard, J. O., and Klee, G. G. (2006) Quality management. In: Burtis, C. A., Ashwood, E. R., and Bruns, D. E., eds. Tietz Textbook of Clinical Chemistry and Molecular Diagnostics, 4th ed. St. Louis, Elsevier Saunders, pp. 485–532.Google Scholar
  7. 7.
    Parvin, C. A. (1997) Quality-control (QC) performance measures and the QC planning process. Clin. Chem. 43, 602–607.PubMedGoogle Scholar
  8. 8.
    Linnet, K. (1991) Mean and variance rules are more powerful or selective than quality control rules based on individual values. J. Clin. Chem. Clin. Biochem. 29, 417–424.Google Scholar
  9. 9.
    Parvin, C. A. (1993) New insight into the comparative power of quality-control rules that use control observations within a single analytical run. Clin. Chem. 39, 440–447.PubMedGoogle Scholar
  10. 10.
    Parvin, C. A., and Gronowski, A. M. (1997) Effect of analytical run length on quality-control (QC) performance and the QC planning process. Clin. Chem. 43, 2149–2154.PubMedGoogle Scholar
  11. 11.
    Parvin, C. A. (1991) Estimating the performance characteristics of quality-control procedures when error persists until detection. Clin. Chem. 37, 1720–1724.PubMedGoogle Scholar
  12. 12.
    Clinical and Laboratory Standards Institute. (2000) How to Define and Determine Reference Intervals in the Clinical Laboratory; Approved GuidelineSecond Edition. C28-A2. Villanova, Clinical and Laboratory Standards Institute.Google Scholar
  13. 13.
    David, H. A., and Nagaraja, H. N. (2003) Order Statistics, 3rd ed. New York, John Wiley & Sons.CrossRefGoogle Scholar
  14. 14.
    Parrish, R. S. (1990) Comparison of quantile estimators in normal sampling. Biometrics 46, 247–257.CrossRefGoogle Scholar
  15. 15.
    Harrell, F. E., and Davis, C. E. (1982) A new distribution-free quantile estimator. Biometrika 69, 635–640.CrossRefGoogle Scholar
  16. 16.
    Solberg, H. E. (2006) Establishment and use of reference values. In: Burtis, C. A., Ashwood, E. R., and Bruns, D. E. eds. Tietz Textbook of Clinical Chemistry and Molecular Diagnostics, 4th ed. St. Louis, Elsevier Saunders, pp. 425–448.Google Scholar
  17. 17.
    Beran, R., and Hall, P. (1993) Interpolated nonparametric prediction intervals and confidence intervals. J. Roy. Statist. Soc. B 55, 643–652.Google Scholar
  18. 18.
    Harris, E. K., and Boyd, J. C. (1995) Statistical Bases of Reference Values in Laboratory Medicine. New York, Marcel Dekker.Google Scholar
  19. 19.
    Reed, A. H., Henry, R. J., and Mason, W. B. (1971) Influence of statistical method used on the resulting estimate of normal range. Clin. Chem. 17, 275–284.PubMedGoogle Scholar
  20. 20.
    Horn, P. S., and Pesce, A. J. (2003) Reference intervals: an update. Clin. Chim. Acta. 334, 5–23.PubMedCrossRefGoogle Scholar
  21. 21.
    Linnet, K. (1987) Two-stage transformation systems for normalization of reference distributions evaluated. Clin. Chem. 33, 381–386.PubMedGoogle Scholar

Copyright information

© Humana Press Inc., Totowa, NJ 2007

Authors and Affiliations

  • Curtis A. Parvin
    • 1
  1. 1.Department of Pathology and ImmunologyWashington University School of MedicineSt. Louis

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