Non-parametric estimation of reference intervals in small non-Gaussian sample sets
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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.
KeywordsReference intervals Bootstrap Re-sampling Algorithm Non-parametric Percentile Confidence intervals Gaussian Distribution
Thanks to the staff at Rikshospitalet-Radiumhospitalet, who provided us access to their computers for simulations during the Easter vacation!
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