ePrints

Understanding the impact of pre-analytic variation in haematological and clinical chemistry analytes on the power of association studies

Lookup NU author(s): Emeritus Professor Paul BurtonORCiD

Downloads

• Published version [.pdf]

Licence

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© The Author 2014; all rights reserved.Background: Errors, introduced through poor assessment of physical measurement or because of inconsistent or inappropriate standard operating procedures for collecting, processing, storing or analysing haematological and biochemistry analytes, have a negative impact on the power of association studies using the collected data. A dataset from UK Biobank was used to evaluate the impact of pre-analytical variability on the power of association studies. Methods: First, we estimated the proportion of the variance in analyte concentration that may be attributed to delay in processing using variance component analysis. Then, we captured the proportion of heterogeneity between subjects that is due to variability in the rate of degradation of analytes, by fitting a mixed model. Finally, we evaluated the impact of delay in processing on the power of a nested case-control study using a power calculator that we developed and which takes into account uncertainty in outcome and explanatory variables measurements. Results: The results showed that (i) the majority of the analytes investigated in our analysis, were stable over a period of 36 h and (ii) some analytes were unstable and the resulting pre-analytical variation substantially decreased the power of the study, under the settings we investigated. Conclusions: It is important to specify a limited delay in processing for analytes that are very sensitive to delayed assay. If the rate of degradation of an analyte varies between individuals, any delay introduces a bias which increases with increasing delay. If preanalytical variation occurring due to delays in sample processing is ignored, it affects adversely the power of the studies that use the data.

Publication metadata

Author(s): Gaye A, Peakman T, Tobin MD, Burton PR

Publication type: Article

Publication status: Published

Journal: International Journal of Epidemiology

Year: 2014

Volume: 43

Issue: 5

Pages: 1633-1644

Online publication date: 31/07/2014

Acceptance date: 01/01/1900

Date deposited: 27/02/2018

ISSN (print): 0300-5771

ISSN (electronic): 1464-3685

Publisher: Oxford University Press

URL: https://doi.org/10.1093/ije/dyu127

DOI: 10.1093/ije/dyu127

PubMed id: 25085103

Altmetrics

Share