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. 2021 Nov 15;13(11):12557-12574.
eCollection 2021.

Modeling factors critical for implementation of precision medicine at health systems-level: an IRT approach

John Jo Mogaka  1 , Moses J Chimbari  1
Affiliations

Modeling factors critical for implementation of precision medicine at health systems-level: an IRT approach

John Jo Mogaka et al. Am J Transl Res. .

Abstract

Background: Through recent advances in omics technologies, precision medicine (PM) promises to fundamentally change the way we approach health, disease and illness. Imperative applications of omics-based biomarkers are gradually moving from research to clinical settings, with huge long-term clinical and public health implications. Whereas much of research in PM is mainly focused on basic biomedical discoveries, currently there is little research on the clinical implementation of omics biomarkers, especially at health systems level.

Aim and methods: This study investigated the application of multidimensional item response theory (IRT) models to validate a hypothesized PM implementation measurement model. This is a contribution to PM implementation at health systems level. Data obtained through an item-sort procedure involving 496 observations from 124 study participants formed the basis of a 22-item PMI measurement model.

Conclusion: Statistical significance of the bifactor model suggests PM implementation may have to be examined using factors that reflect a single common underlying implementation construct, as well as factors that reflect unique variances for the identified four content-specific factors.

Keywords: IRT; Precision medicine; bifactor modelling; implementation model; omics technologies.

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Conflict of interest statement

None.

Figures

Figure 1
Figure 1
Five of the main omics technologies and corresponding biomarkers: Genomics: DNA sequences using genomic DNA (gDNA-sequencing); Transcriptomics: messenger RNA (mRNA) sequencing and mRNA expression level using RNA-sequencing; Epigenomics: DNA methylation and histone modification using ChIP-sequencing; Proteomics: protein sequence using liquid chromatography and mass spectrometry; Metabolomics: metabolites using mass spectrometry and NRM.
Figure 2
Figure 2
Demographic characteristics of study participants.
Figure 3
Figure 3
Quantile-Quantile (Q-Q) plot describing distribution of data and Mahabolis distances.
Figure 4
Figure 4
Variable correlation matrix.
Figure 5
Figure 5
LD values for three IRT models.
See this image and copyright information in PMC

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