Complex methods for complex data: key considerations for interpretable and actionable results in exposome research

Area of Science:

• Epidemiology
• Data Science
• Public Health

Background:

• Increasing availability of complex, multidimensional data is transforming epidemiological study design.
• The exposome framework offers opportunities to redefine public health recommendations at individual and population levels.
• Handling complex data necessitates advanced analytical approaches like machine learning.

Purpose of the Study:

• To provide an overview of three key levels of interpretability: statistical, causal, and actionable.
• To discuss tools that assist epidemiologists in interpreting complex analytical results.
• To enhance the application of epidemiological findings for tangible interventions.

Main Methods:

• Overview of semi-parametric and non-parametric statistical methods.
• Discussion of machine learning methodologies for large-scale databases.
• Exploration of interpretability frameworks for complex epidemiological data.

Main Results:

• Interpreting complex analytical methods presents challenges beyond statistical inference.
• Causal considerations and practical applicability are crucial but often overlooked aspects of interpretability.
• A multi-level approach to interpretability (statistical, causal, actionable) is essential.

Conclusions:

• Epidemiologists need to address statistical, causal, and actionable interpretability for complex data.
• Utilizing advanced analytical approaches requires robust interpretation strategies.
• Improved interpretability can lead to more effective public health interventions.