Sensitivity and specificity of information criteria

Area of Science:

• Biostatistics
• Health Research
• Biological Research

Background:

• Information criteria (ICs) are standard for model selection in health and biological research.
• Discrepancies between criteria like AIC and BIC can cause ambiguity in model choice.
• Current usage often lacks clear justification for selecting specific criteria.

Purpose of the Study:

• To offer a novel perspective on interpreting information criteria (ICs) for model selection.
• To clarify the practical implications of using different ICs.
• To provide a framework for informed decision-making when ICs yield conflicting results.

Main Methods:

• The study frames model comparison using ICs as equivalent to likelihood ratio tests.
• It analyzes the relationship between AIC and BIC in terms of statistical conservatism.
• The approach facilitates understanding IC behavior in complex modeling scenarios.

Main Results:

• Information criteria can be interpreted as likelihood ratio tests with varying alpha levels.
• Bayesian information criterion (BIC) functions as a more conservative test than Akaike's information criterion (AIC).
• The choice between AIC and BIC may depend on prioritizing sensitivity versus specificity.

Conclusions:

• Understanding ICs as likelihood ratio tests enhances interpretation of their practical implications.
• This perspective aids in resolving ambiguities when different criteria suggest different models.
• Informed decisions regarding model selection can be made by understanding IC similarities and differences.