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Identification of an additive interaction using parameter regularization and model selection in epidemiology.

Chanchan Hu1, Zhifeng Lin1, Zhijian Hu1,2

  • 1Department of Epidemiology and Health Statistics, Fujian Medical University, Fuzhou, Fujian, China.

Peerj
|October 18, 2024
PubMed
Summary
This summary is machine-generated.

New methods ensure consistent results when assessing additive interactions using relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (S). These approaches simplify interpretation for epidemiological studies.

Keywords:
Additive interactionsEpidemiologyModel selectionParameter regularizationReal data

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Commonly used indicators for additive interactions (RERI, AP, S) can yield inconsistent results in practice.
  • Difficulty in drawing clear conclusions from existing interaction assessment methods.

Purpose of the Study:

  • To propose a novel method for assessing additive interactions with consistent and clear results.
  • To provide two pathways for achieving consistent interaction assessment: penalized likelihood and model selection.

Main Methods:

  • Constraining parameters based on the relationship between RERI, AP, and S.
  • Utilizing a regular penalty term in the model likelihood function.
  • Employing model selection techniques, including the Hannan-Quinn criterion (HQ).

Main Results:

  • Proposed methods effectively identified additive interactions in both simulated and real-world data.
  • Regularized estimation demonstrated convergence and accurate identification of interaction status.
  • Model selection, particularly HQ, showed competitive performance against bootstrap methods.

Conclusions:

  • The Hannan-Quinn criterion-based model selection is a strong alternative to bootstrap for additive interaction identification.
  • The proposed methods lead to more consistent and easily interpretable results for RERI, AP, and S.