On the role of benchmarking data sets and simulations in method comparison studies

Area of Science:

• Statistical methodology
• Machine learning evaluation

Background:

• Method comparison studies are crucial for applied researchers selecting appropriate approaches.
• Existing comparisons often lack neutrality, favoring novel methods.
• Current studies predominantly use simulation or real-world benchmarking data, with limited integration.

Purpose of the Study:

• To investigate the differences and similarities between simulation studies and benchmarking data for method evaluation.
• To discuss the advantages and disadvantages of each approach.
• To develop novel evaluation methods by integrating the strengths of both simulation and benchmarking.

Main Methods:

• Comparative analysis of simulation studies and real-world benchmarking datasets.
• Exploration of mixed-methods research principles.
• Application of Clinical Scenario Evaluation concepts.

Main Results:

• Simulation studies offer controlled environments but may lack real-world complexity.
• Benchmarking datasets provide real-world relevance but can be biased by community adoption.
• A hybrid approach can potentially offer more robust and generalizable method evaluations.

Conclusions:

• Neither simulation nor benchmarking alone is sufficient for comprehensive method evaluation.
• Integrating insights from both approaches can lead to more reliable guidance for researchers.
• Future work should focus on developing and validating these hybrid evaluation frameworks.