Helping reviewers assess statistical analysis: A case study from analytic methods.

Area of Science:

• Statistical methodology
• Applied statistics
• Analytical method development

Background:

• Data analysis and experimental methods are crucial in analytic methods development.
• Current review practices for statistical contributions lack structured guidelines, leading to variability.
• The rise of machine learning and complex algorithms exacerbates evaluation challenges.

Purpose of the Study:

• To provide structured checklists for reviewers of applied statistics.
• To focus on enhancing the review process for analytical method development.
• To address the need for consistent and reliable assessment of statistical analyses.

Main Methods:

• Development of checklists covering six key aspects of statistical review.
• Consideration of study design, frequentist and Bayesian methods, selective inference, severe testing, and presentation.
• Illustration of checklist application using robustness analysis of an analytical method.

Main Results:

• Checklists offer a structured approach to statistical assessment.
• The proposed checklists address challenges in evaluating complex analytical methods.
• Robustness analysis demonstrates practical improvement through checklist utilization.

Conclusions:

• Structured checklists can significantly reduce uncertainty and variability in statistical reviews.
• The developed checklists provide a framework for rigorous evaluation of analytical method development.
• These guidelines are valuable for engineers and researchers involved in statistical analysis.