In multilaboratory clinical studies, laboratory differences cannot be ignored when analyzing data from different laboratories together. In this paper, we propose a new method to transform data across laboratories. The method uses established empirical distributions of assay results from different laboratories for the transformation. This transformation does not rely on either the distributional assumption of the assay reading data or the reference ranges that are required by commonly-used standardization methods. A simulation study shows that the mean and standard deviation of the transformed data are close to the true values. A clinical example is given to compare the usual standardization method to our method. In general, our method is shown to be useful in combining data from multiple laboratories, especially when the data are nonnormal and when the reference ranges are not reliable. Finally, the pros and cons of the method are discussed.
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Huang, J., Brunelle, R. A Nonparametric Method for Combining Multilaboratory Data. Ther Innov Regul Sci 36, 395–406 (2002) doi:10.1177/009286150203600219
- Cumulative density function
- Reference ranges
- Empirical distribution