A more flexible parametric estimation of univariate reference intervals: a new method based on the GS-distribution.
BACKGROUND
Reference interval estimation is an important issue in clinical laboratories. Present methods are based either on data transformation or on non-parametric approaches.


METHODS
We present a new technique based in a family of statistical distributions known as GS-distributions that provide a suitable model for continuous unimodal variables. We compare, both by simulation studies an on actual data, the reference intervals estimated by using non-parametric methods and data transformations suggested by the IFCC and those obtained by fitting a GS-distribution. Simulated data are generated from various distributions to evaluate the accuracy of these methods. In each case, confidence intervals for the resulting reference intervals are obtained by bootstrap.


RESULTS
In all the cases, the GS-distribution based method provides comparable or more accurate results than the non-parametric methods. In most cases, the proposed method produces better results than those obtained by transforming the original data.


CONCLUSIONS
Our results suggest that the method for computing reference intervals based on GS-distribution is a valid alternative for the current non-parametric methods.
Trujillano
Javier
J
Grup de Bioestadística i Biomatemàtica, Departament de Ciències Mèdiques Bàsiques, Institut de Recerca Biomèdica de Lleida, Universitat de Lleida, Montserrat Roig, 2, 25008-Lleida, Spain.
Muiño
José M
JM
March
Jaume
J
Sorribas
Albert
A
D019411:Clinical Laboratory Techniques / Q000592:standards*; D003198:Computer Simulation; D016001:Confidence Intervals; D016011:Normal Distribution; D012016:Reference Values