Reference Interval Computation Using Robust vs Parametric and Nonparametric Analyses

The clinical chemist is faced with the problem of defining reference intervals for many analytes. Problems that hinder such a determination are the presence of outliers in the data set and the inability to accumulate the recommended sample size (1). We previously have demonstrated the theoretical basis for the application of robust methods to resolve these problems (2). In particular, we consider the problem of establishing that the reference population is “healthy” to be a nearly impossible task because many disease processes may be missed in the examination process. Our own experience shows that diabetics were initially classified as healthy in our test population, and only after a thorough review of all of the data was it possible to elicit the presence of this disease. In this report, we propose the use of a robust estimator we have described previously (2). The advantage of this approach is that it is more tolerant of outliers in the reference population data and does not require as large a sample size as the nonparametric calculation method, nor does it require the reference data to be transformed to a gaussian distribution, which is not always possible. We then apply and compare this robust estimator with both the traditional nonparametric and parametric analysis in determining reference intervals for a well-studied population. The Fernald Medical Monitoring Program provided us with a documented healthy sample (T) to test the three methods: parametric, nonparametric, and robust. Our computer-generated sample (W) offered the possibility to test our estimates of reference intervals in a population with a greater potential incidence of diseases (3). The robust approach offered the opportunity to look at …