A general framework for a reliable multivariate analysis and pattern recognition in high-dimensional epidemiological...

Area of Science:

• Epidemiology
• Biostatistics
• Data Science

Background:

• Classical epidemiological tools include comparisons of means/proportions, regression models (linear, logistic, Cox), and their multivariate formulations.
• Natively massive multivariate techniques, such as clustering, are underutilized in epidemiology despite weaker assumptions and pattern recognition capabilities.
• Clustering is widely applied in genetics and biomolecular studies for identifying homogeneous groups without a priori knowledge.

Purpose of the Study:

• To introduce epidemiologists to underutilized multivariate clustering techniques.
• To explain the core principles of clustering, including proximity definition and optimal group number determination.
• To present archetypal algorithms like k-means and PAM and their relation to classical statistical methods.

Main Methods:

• Clustering techniques require parameter tuning, notably the number of groups to discover.
• Approaches like the silhouette and robustness methods aid in finding the optimal number of groups.
• The study details key aspects of clustering: defining observation proximity and determining the number of groups, using k-means and PAM as examples.

Main Results:

• A framework is presented to reconsider epidemiological concerns using clustering.
• The study demonstrates how to identify group existence, determine the optimal number of groups, and label observations.
• Methods for analyzing identified groups with explicative data and achieving consistent, initialization-insensitive results are shown.

Conclusions:

• Clustering techniques, combined with parameter tuning, offer substantial new tools for epidemiologists.
• Unlike hypothesis-testing approaches, clustering methods make no assumptions on data and are natively multivariate.
• These techniques enable pattern recognition and homogeneous group retrieval, expanding the epidemiologist's analytical capabilities.