Estimation of normal ranges of clinical laboratory data

This paper proposes a new procedure for estimating normal ranges of clinical laboratory tests, which can be applied to data with possibly more than one outlier from healthy subjects. The proposed procedure determines the optimal model among a class of models in which it is assumed that an observed distribution of 'normal values' can be transformed to the Gaussian form by one of several specified transformations, and if there exist outliers among the data, then each of the transformed outliers also follows a Gaussian distribution with different mean from, but the same variance as, the transformed distribution of normal values. The optimal model is defined as the best combination of the transformation to normality and the number of outliers identified, and is selected by the Akaike information criterion (AIC). Our procedure is illustrated with data from 200 healthy male subjects on 25 laboratory tests.