Performance of two new algorithms for estimating within- and between-method carryover evaluated statistically.
Accurate and precise algorithms for estimating within-method carryover, based on the minimization of a unique "carryover sum of squares," and between-method carryover, based on a weighted Deming regression of first sample recovery vs carryover-corrected "true" recovery, are described and compared with traditional methods by use of a Monte Carlo study. In addition, I have studied the experimental parameters that influence the accuracy and precision of carryover estimation. The new algorithm for estimating within-method carryover is unbiased under most conditions, whereas the traditional algorithm is biased low under most conditions. The new algorithm is also more precise, owing to more-efficient utilization of information contained in an analytical run performed for carryover estimation. Between-method carryover in a random-access analyzer is estimated quantitatively by the second proposed algorithm and is found to be readily and precisely determinable. Use of these methods in combination to evaluate analytical interaction should allow the prediction of carryover error under most current analytical situations.
Department of Biochemistry, Lilly Research Laboratories, Eli Lilly and Co., Lilly Corporate Center, Indianapolis, IN 46285.
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Stephens
D000465:Algorithms; D002624:Chemistry, Clinical / Q000379:methods*; D011786:Quality Control; D012044:Regression Analysis; D012984:Software; D013223:Statistics as Topic