A simple methodology to analyze inter-laboratory data: a simulation study.
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.
Division of Laboratory Statistics, National Center for Environmental Health, Centers for Disease Control and Prevention, Mail Stop F-47, Atlanta, GA 30341, United States. Rij0@cdc.gov
Ram B
RB
Jain
D000465:Algorithms; D019411:Clinical Laboratory Techniques / Q000592:standards*; D008722:Methods; D015588:Observer Variation; D015203:Reproducibility of Results; D012106:Research / Q000592:standards*; D018401:Sample Size