Improved allocation of costs through analysis of variation in data: planning of laboratory studies.
BACKGROUND
When developing a new laboratory test for study of human diseases, it is important to identify and control internal and external sources of variation that affect test results. It is also imperative that the precision of the test not only meets pre-established requirements and not exceed allowable total error, but also that these objectives are reached without undue expenditure of either time or financial resources.


METHODS
This study applies statistical principles in designing a cost-effective experimental approach for determining the analytical precision of a new test. This approach applies the statistical concept of variance components to the problem of balancing a pre-established level of analytical precision against expenses incurred in achieving this precision.


RESULTS
We demonstrated (1) estimation of variance components, (2) use of these estimates for improving allocation of costs within the experiment, and (3) use of these estimates for determining the optimal number of replicate measurements.


CONCLUSIONS
Although elimination of all sources of variation that can affect laboratory test results is unlikely, the application of analysis of variance (ANOVA) statistical techniques can lead to a cost-effective allocation of resources for estimating the precision of a laboratory test.
Foster
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TA
Department of Biostatistics, School of Public Health and Tropical Medicine, Tulane University Medical Center, New Orleans, LA 70112, USA. tfoster1@mailhost.tcs.tulane.edu
Asztalos
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BF
D000465:Algorithms; D000704:Analysis of Variance; D002138:Calibration; D002784:Cholesterol / Q000097:blood; D019411:Clinical Laboratory Techniques / Q000191:economics* / Q000706:statistics & numerical data*; D003361:Cost Allocation / Q000706:statistics & numerical data*; D003362:Cost-Benefit Analysis; D003627:Data Interpretation, Statistical; D006801:Humans; D007202:Indicators and Reagents / Q000191:economics; D012107:Research Design; D018401:Sample Size
D007202:Indicators and Reagents; D002784:Cholesterol