Abstract
In the era of precision health, nursing research has increasingly focused on the analysis of large, multidimensional data sets containing multiple correlated phenotypes (e.g., symptoms). This presents challenges for statistical analyses, especially in genetic association studies. For example, the inclusion of multiple symptoms within a single model can raise concerns about multicollinearity, while individual SNP-symptom analyses may obscure complex relationships. As such, many traditional statistical approaches often fall short in providing a comprehensive understanding of the complexity inherent in many nursing-focused research questions. Multivariate Bayesian approaches offer the unique advantage of allowing researchers to ask questions that are not feasible with traditional approaches. Specifically, these methods support the simultaneous exploration of multiple phenotypes, accounting for the underlying correlational structure between variables, and allow for formal incorporation of existing knowledge into the statistical model. By doing so, they may provide a more realistic view of statistical relationships within a biological system, potentially uncovering new insights into well-established and undiscovered connections, such as the probabilities of association and direct versus indirect effects. This valuable information can help us better understand our phenotypes of interest, leading to more effective nurse-led intervention and prevention programs. To illustrate these concepts, this paper includes an application section covering two specific multivariate Bayesian analysis software programs, bnlearn and mvBIMBAM, with an emphasis on interpretation and extension to nursing research. To complement the paper, we provide access to a detailed online tutorial, including executable R code and a synthetic data set, so the concepts can be more easily extended to other research questions.