A ROBUST APPROACH TO REFERENCE INTERVAL ESTIMATION AND EVALUATION

We propose a new methodology for the estimation of reference intervals for data sets with small numbers of observations or for those with su bstantial numbers of outliers. We propose a prediction interval that u ses robust estimates of location and scale. The SAS software can be re adily modified to do these calculations. We compared four reference in terval procedures (nonparametric, transformed, robust with a nonparame tric lower limit, and transformed robust) for sample sizes of 20, 40, 60, 80, 100, and 120 from chi(2) distributions of 1, 4, 7, and 10 df. chi(2) distributions were chosen because they simulate the skewness of distributions often found in clinical chemistry populations. We used the root mean square error as the measure of performance and used comp uter simulation to calculate this measure. The robust estimator showed the best performance for small sample sizes. As the sample size incre ased, the performance values converged. The robust method for calculat ing upper reference interval values yields reasonable results. In two examples using real data for haptoglobin and glucose, the robust estim ator provides slightly smaller upper reference limits than the other p rocedures. Lastly, the robust estimator was compared with the other pr ocedures in a population where 5% of the values were multiplied by a f actor of 5. The reference intervals were calculated with and without o utlier detection. In this case, the robust approach consistently yield ed upper reference interval values that were closer to those of the tr ue underlying distributions. We propose that robust statistical analys is can be of great use for determinations of reference intervals from limited or possibly unreliable data.