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Metabolomics. 2019 Jan 3;15(1):7. doi: 10.1007/s11306-018-1463-4.

Avoiding selection bias in metabolomics studies: a tutorial.

Author information

1
Department of Clinical Epidemiology, Department C7-P, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC, Leiden, The Netherlands. s.c.boone@lumc.nl.
2
Department of Clinical Epidemiology, Department C7-P, Leiden University Medical Center (LUMC), PO Box 9600, 2300 RC, Leiden, The Netherlands.
3
Department of Biomedical Data Sciences, Section Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
4
Department of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands.
5
Einthoven Laboratory for Experimental Vascular Medicine, Leiden University Medical Center, Leiden, The Netherlands.
6
Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands.
7
Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, The Netherlands.

Abstract

BACKGROUND:

Metabolomics techniques are increasingly applied in epidemiologic research. Many available assays are still relatively expensive and therefore measurements are often performed in small patient population studies such as case series or case-control designs with strong participant selection criteria. Subsequently, metabolomics data are frequently used to assess secondary associations for which the original study was not explicitly designed. Especially in these secondary analyses, there is a risk that the original selection criteria and the conditioning that takes place due to this selection are not properly accounted for which can lead to selection bias.

AIM OF REVIEW:

In this tutorial, we start with a brief theoretical introduction on the issue of selection bias. Subsequently, we demonstrate how selection bias can occur in metabolomics studies by means of an investigation into associations of metabolites with total body fat in a nested case-control study that was originally designed to study effects of elevated fasting glucose.

KEY SCIENTIFIC CONCEPTS OF REVIEW:

We demonstrate that standard analytical methods, such as stratification or adjustment in regression analyses, are not suited to deal with selection bias and may even induce the bias when analysing metabolite-phenotype relationships in selected groups. Finally, we show that inverse probability weighting, also known as survey weighting, can be used in some situations to make unbiased estimates of the outcomes.

KEYWORDS:

Collider bias; Epidemiology; Inverse probability weighting; Metabolomics; Selection bias

PMID:
30830435
DOI:
10.1007/s11306-018-1463-4

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