Non-parametric estimation of reference intervals in small non-Gaussian sample sets
2009 (English)In: ACCREDITATION AND QUALITY ASSURANCE, ISSN 0949-1775, Vol. 14, no 4, 185-192 p.Article in journal (Refereed) Published
This study aimed at validating common bootstrap algorithms for reference interval calculation.We simulated 1500 random sets of 50-120 results originating from eight different statistical distributions. In total, 97.5 percentile reference limits were estimated from bootstrapping 5000 replicates, with confidence limits obtained by: (a) normal, (b) from standard error, (c) bootstrap percentile (as in RefVal) (d) BCa, (e) basic, or (f) student methods. Reference interval estimates obtained with ordinary bootstrapping and confidence intervals by percentile method were accurate for distributions close to normality and devoid of outliers, but not for log-normal distributions with outliers. Outlier removal and transformation to normality improved reference interval estimation, and the basic method was superior in such cases. In conclusions, if the neighborhood of the relevant percentile contains non-normally distributed results, bootstrapping fails. The distribution of bootstrap estimates should be plotted, and a non-normal distribution should warrant transformation or outlier removal.
Place, publisher, year, edition, pages
2009. Vol. 14, no 4, 185-192 p.
Reference intervals, Bootstrap, Re-sampling, Algorithm, Non-parametric, Percentile, Confidence intervals, Gaussian, Distribution
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:liu:diva-17911DOI: 10.1007/s00769-009-0490-2OAI: oai:DiVA.org:liu-17911DiVA: diva2:212999