Abstract :
Laboratory analyses represent a key element in veterinary medicine diagnosis providing
objective information about the health status of a patient. Analytic data are interpreted by
comparing them with a specific reference intervals previously determined on a reference
population of healthy animals. The International Federation of Clinical Chemistry recommends
the use of nonparametric methods and, as a consequence, a reference sample of at
least 120 healthy subjects, to obtain reliable reference intervals. Such order of magnitude for
the reference sample is not always feasible especially if the laboratory variable under study
is affected by several sources of variation, e.g., environmental conditions, physiological status
of the animal, age, or gender.Aconvenientmethod to estimate reference intervals should
be able to avoid assumptions on the probability distribution of the considered variable and
produce robust results even with a limited sample size.
This study presents a new statistical approach, based on data bootstrap, to estimate reference
intervals for 12 blood biochemical variables in Sarda dairy sheep. The method was
applied to real and simulated data from 120 to 40 animals. The reference intervals calculated
with the new method remained quite constant as sample size decreased from 120
down to 60 animals, and became wider with fewer individuals. So, a minimum threshold of
60 animals could be considered a good limit to obtain reliable reference intervals for blood
biochemical variables in Sarda dairy sheep. Moreover, comparisons between results from
real and simulated data suggested that the method could be also applied to other laboratory
variables.
