Multivariate Bayesian Analyses in Nursing Research: An Introductory Guide.

Area of Science:

• Nursing Research
• Biostatistics
• Genetics
• Precision Health

Background:

• Nursing research increasingly uses large, multidimensional datasets with correlated phenotypes, posing statistical challenges.
• Traditional statistical methods struggle with multicollinearity and obscured complex relationships in genetic association studies.
• A comprehensive understanding of complex biological systems is often limited by conventional analytical approaches.

Purpose of the Study:

• To introduce multivariate Bayesian approaches as a powerful tool for nursing research.
• To demonstrate how these methods can simultaneously explore multiple phenotypes and their correlations.
• To highlight the potential for uncovering novel insights into biological systems and informing nurse-led interventions.

Main Methods:

• Application of multivariate Bayesian methods to analyze complex, correlated phenotypes.
• Simultaneous exploration of multiple phenotypes, accounting for correlational structures.
• Incorporation of prior knowledge into statistical models for a more realistic biological system view.
• Utilizing specific software programs like bnlearn and mvBIMBAM for analysis.

Main Results:

• Multivariate Bayesian approaches facilitate the exploration of complex relationships between phenotypes.
• These methods allow for the estimation of association probabilities and direct/indirect effects.
• The approach provides a more realistic view of statistical relationships within biological systems.
• Uncovering potential new insights into established and undiscovered connections.

Conclusions:

• Multivariate Bayesian methods offer significant advantages over traditional approaches for nursing research.
• These methods can lead to a better understanding of phenotypes, improving nurse-led intervention and prevention programs.
• The paper provides practical tools and examples for extending these analyses to nursing research questions.