A simple methodology to analyze inter-laboratory data: A simulation study

作者：Jain Ram B^*

来源：Clinica Chimica Acta, 2009, 410(1-2): 79-84.

DOI：10.1016/j.cca.2009.09.027

• Inter-laboratory experiments
• Bias
• Precision
• Repeatability
• Reproducibility
• Homogeneity of variance

Background: Inter-laboratory experiments are conducted to assess how accurately and reproducibly various laboratories using different methods, instruments, analysts, and/or sample preparation procedures can perform measurements, in this case, the concentration of a chemical. In this work a 3-step methodology is proposed to analyze inter-laboratory experiments.
Methods: A simulation study based on 500 simulations was conducted for a cluster of 12 laboratories with different population means and SDs. The sample sizes varied from 10 to 50. Laboratories with too high a variance or too high a mean were recursively identified and removed by analysis of variance techniques. Outliers were identified and removed by a recursive algorithm. The remaining data were used to compute consensus mean, SD, repeatability, reproducibility, and CV for repeatability and reproducibility.
Results: Two laboratories with too high a variance were always identified for removal when the sample size was >= 20. Two laboratories with too high a mean were almost always identified irrespective of sample size. The average observed percent bias was never > +/- 3.2% irrespective of the sample size. The average percent imprecision was also within +/- 10.4% for all laboratories. The average CV was close to what was expected.
Conclusions: With an optimal sample size of 20, the 3-step methodology presented here will adequately identify laboratories with variances or means that are too high or too low. Published by Elsevier B.V.

• 出版日期2009-12-8