Laboratory data reveal diverse distribution patterns: Mapping individual variability for personalized laboratory...

Area of Science:

• Biostatistics
• Clinical Chemistry
• Data Science

Background:

• Laboratory data often assumed to be normal or log-normal.
• Non-parametric methods used when standard distributions fail.
• Existing assumptions may limit accurate data analysis.

Purpose of the Study:

• To empirically analyze measurement result distributions.
• To identify the best-fitting distribution types for laboratory data.
• To challenge the reliance on only normal or log-normal models.

Main Methods:

• Analysis of repeated measurements for glucose, creatinine, ALT, bilirubin, and calcium.
• Inclusion of data from 41 individuals over 5 weeks.
• Evaluation of 105 distribution models using SciPy.

Main Results:

• Logistic distribution best fit for glucose.
• Laplace-asymmetric distribution identified for ALT.
• Lévy-stable distribution found optimal for creatinine, total bilirubin, and calcium.

Conclusions:

• A data-driven empirical approach is superior for laboratory data analysis.
• Best-fitting distributions should be identified without theoretical bias.
• This method enhances the accuracy of laboratory data interpretation.