A bootstrap approach to estimate reference intervals of biochemical variables in sheep using reduced sample sizes

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.