Measurement Of Thyroid-Stimulating Hormone Receptor Autoantibodies By Elisa

using the nonparametric procedure. The other three procedures provided similar, although slightly narrower, intervals. (All of the tables are available by request. Each table consists of 27 analytes, 5 of which are hematological.) Where a transformation of the data for a particular test to a gaussian distribution was not possible or if the Anderson-Darling test rejected normality, the reference interval was not computed and the appropriate cell was left blank. It is noteworthy that in approximately one-third of the cases, no suitable transformation was found to allow for use of traditional normal theory methods. However , it is reassuring that when a successful transition was achieved, the resulting reference interval was comparable to that of the transformed robust (which requires only symmetry) as well as the untransformed robust method. It is also noteworthy that the robust method, although designed primarily for right-skewed populations, gives results that are comparable to the other, traditional methods for left-skewed data such as hemoglobin. The reference interval width for the W group was compared to that of the T group by using their ratio because this dimensionless quantity allows for comparisons across analytes. For the 27 analytes tested, the interval width for the W data was on average 25% wider than the interval width based on the T data using either the nonparametric or robust analysis. To evaluate the effect of using different methods of estimating the reference interval, the ratio of the interval width calculated by the nonparametric procedure to that of the robust procedure was examined. For the 27 analytes tested, the non-parametric interval was 2.7% wider (P Ͻ0.01) for the T group and 3.7% wider (P Ͻ0.01) for the W group. The smaller percentage of difference in the carefully screened and documented healthy T group shows that under these conditions, the robust and nonparametric methods are in better agreement. These observations are consistent with our simulation data. Our recommendation for the development of reference intervals includes the use of both nonparametric and robust estimators where the relationship between the intervals estimated by the two methods can be used as an " ad hoc " estimate of the reference population quality. Evaluation of the reference intervals by both of these techniques should yield similar results. If they do not agree, then consideration must be given to the possibility that a significant part of the sample could have diseased individuals or that multiple populations …