Are Your Laboratory Data Reproducible? The Critical Role of Imprecision from Replicate Measurements to Clinical Decision-making

Abstract

Measurement results of biological samples are not perfect and vary because of numerous factors related to the biological samples themselves and the measurement procedures used to analyze them. The imprecision in patients' laboratory data arising from the measurement procedure, known as analytical variation, depends on the conditions under which the data are collected. Additionally, the sample type and sampling time significantly affect patients' laboratory results, particularly in serial measurements using samples collected at different time points. For accurate interpretation of patients' laboratory data, imprecision-both its analytical and biological components-should be properly evaluated and incorporated into data management. With advancements in measurement technologies, analytical imprecision can be minimized to an insignificant level compared to biological imprecision, which is inherent to all biomolecules because of the dynamic nature of metabolism. This review addresses: (i) the theoretical background of variation, (ii) the statistical and metrological evaluation of measurement variation, (iii) the assessment of variation under different conditions in medical laboratories, (iv) the impact of measurement variation on clinical decisions, (v) the influence of biases on measurement variation, and (vi) the variability of analytes in human metabolism. Collectively, both analytical and biological imprecision are inseparable aspects of all measurements in biological samples, with biological imprecision serving as the foundation of personalized laboratory medicine.

Keywords: Analytical variation; Bias; Biological variation; Imprecision; Measurement uncertainty; Precision.

Fig. 1. Concentration and activity of biomolecules in human metabolism are influenced by numerous types of variation. Processing variation relates to the measurement system rather than human metabolism. This figure is modified and reprinted with permission from [98] (copyright © 2024 Walter de Gruyter GmbH).

Fig. 2. Sources of analytical variation under different measurement conditions, including repeatability conditions (A), intermediate precision conditions (B), and reproducibility conditions (C).

Fig. 3. Various types of distribution used in statistical analysis: normal distribution (A), truncated normal distribution (B), semicircular distribution (C), and a hypothetical distribution with lower and upper limits (D). For laboratory data, the distribution in (D) and its skewed variations appear to provide a more realistic representation. The figure is reprinted from [23] and used under the CC-BY license.

Fig. 4. Imprecision of laboratory data arises from biological imprecision, referred to as within-subject or within-person biological variation, and analytical imprecision.