Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Clinical Chemistry and Laboratory Medicine (CCLM)

Published in Association with the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM)

Editor-in-Chief: Plebani, Mario

Ed. by Gillery, Philippe / Lackner, Karl J. / Lippi, Giuseppe / Melichar, Bohuslav / Payne, Deborah A. / Schlattmann, Peter / Tate, Jillian R.

12 Issues per year

IMPACT FACTOR 2016: 3.432

CiteScore 2016: 2.21

SCImago Journal Rank (SJR) 2016: 1.000
Source Normalized Impact per Paper (SNIP) 2016: 1.112

See all formats and pricing
More options …
Volume 43, Issue 11 (Nov 2005)


Mean and variance quality control for multiple correlated levels of replicated control samples

John H. Livesey
  • Endolab, Christchurch Hospital, Christchurch, New Zealand
Published Online: 2011-09-21 | DOI: https://doi.org/10.1515/CCLM.2005.215


Mean and variance rules for quality control are more powerful than rules based on individual values. An algorithm for applying such rules is described that controls type I errors (false alarms), while allowing for multiple levels of quality control samples, correlation between levels, small numbers of preliminary values, replication of samples and autocorrelation arising from random effects. Based on ANOVA and empirical approximations for small samples, the algorithm maintains a low per-batch probability of type I errors. Three statistics are computed, zm, zb and zw, which are shown by simulations to be primarily sensitive to a concordant shift in the quality control values, a discordant shift in the values, and an increase in random variability, respectively. Simulations also show that for a Gaussian distribution of analytical errors, the per-batch probability of a type I error is likely to be within the range 0.0045–0.0071 for two to four levels where there are 20–100 preliminary batches and the inter-level correlations are between zero and 0.8. This partial separation of out-of-control alarms into three components provides more assistance with trouble-shooting than do multivariate quality control schemes based on Hotelling's T2, while retaining comparable power.

Keywords: biometry; clinical chemistry; laboratory techniques and procedures; quality control


  • 1.

    Shewhart WA. Economic control of quality of manufactured product. New York: Van Nostrand, 1931:501 pp.Google Scholar

  • 2.

    Levey S, Jennings ER. The use of control charts in the clinical laboratory. Am J Clin Pathol 1950; 20:1059–66.Google Scholar

  • 3.

    Westgard JO, Barry PL, Hunt MR, Groth T. A multi-rule Shewhart chart for quality control in clinical chemistry. Clin Chem 1981; 27:493–501.Google Scholar

  • 4.

    Montgomery DC. Introduction to statistical quality control, 4th ed. New York: Wiley, 2001:796 pp.Google Scholar

  • 5.

    Linnet K. Mean and variance rules are more powerful or selective than quality control rules based on individual values. Eur J Clin Chem Clin Biochem 1991; 29:417–24.Google Scholar

  • 6.

    Parvin CA. New insight into the comparative power of quality-control rules that use control observations within a single analytical run. Clin Chem 1993; 39:440–7.Google Scholar

  • 7.

    Jansen RT, Laeven M, Kardol W. Internal quality control system for non-stationary, non-ergodic analytical processes based on exponentially weighted estimation of process means and process standard deviation. Clin Chem Lab Med 2002; 40:616–24.CrossrefGoogle Scholar

  • 8.

    Dechert J, Case KE. Multivariate approach to quality control in clinical chemistry. Clin Chem 1998; 44:1959–63.Google Scholar

  • 9.

    Marquis P, Masseyeff R. Évaluer une méthode contrôle de qualité interne: application au contrôle multidimensionel. Ann Biol Clin (Paris) 2000; 60:607–16.Google Scholar

  • 10.

    Wolf FM. Meta-analysis: quantitative methods for research synthesis. Beverley Hills, CA: Sage, 1986:65 pp.Google Scholar

  • 11.

    Scholz FW, Tosch TJ. Small sample uni- and multivariate control charts for means. In: 1994 Proceedings of the Section on Quality and Productivity of the American Statistical Association. Alexandria, VA: American Statistical Association, 1994.Google Scholar

  • 12.

    Press WH, Teukolsky SA, Vettering WT, Flannery BP. Numerical recipes in C: the art of scientific computing, 2nd ed. Cambridge, UK: Cambridge University Press, 1992:994 pp.Google Scholar

  • 13.

    Abramowitz M, Stegun I, editors. Handbook of mathematical functions. New York: Dover, 1965:1046 pp.Google Scholar

  • 14.

    Ryan TP. Statistical methods for quality improvement, 2nd ed. New York: Wiley, 2000:555 pp.Google Scholar

  • 15.

    Westgard JO. Internal quality control: planning and implementation strategies. Ann Clin Biochem 2003; 40:593–611.CrossrefGoogle Scholar

  • 16.

    Jones RD. Reevaluation of the power of error detection of Westgard multirules. Clin Chem 2004; 50:762–4.CrossrefGoogle Scholar

  • 17.

    Prabhu SS, Runger GC. Designing a multivariate EWMA control chart. J Qual Technol 1997; 29:8–15.Google Scholar

  • 18.

    Pignatiello J, Runger G. Comparisons of multivariate CUSUM charts. J Qual Technol 1990; 22:173–86.Google Scholar

  • 19.

    Cembrowski GS. Thoughts on quality-control systems: a laboratorian's perspective. Clin Chem 1997; 43:886–92.Google Scholar

About the article

Corresponding author: John H. Livesey, Endolab, Christchurch Hospital, Private Bag 4710, Christchurch, New Zealand Fax: +64-3-3640818,

Received: 2005-04-14

Accepted: 2005-08-18

Published Online: 2011-09-21

Published in Print: 2005-11-01

Citation Information: Clinical Chemistry and Laboratory Medicine (CCLM), ISSN (Online) 1437-4331, ISSN (Print) 1434-6621, DOI: https://doi.org/10.1515/CCLM.2005.215.

Export Citation

©2005 by Walter de Gruyter Berlin New York. Copyright Clearance Center

Comments (0)

Please log in or register to comment.
Log in