A likelihood approach to proper analysis of secondary outcomes in matched case-control studies

2026-02-22

AI Summary

In matched case-control studies, conventional statistical analyses of secondary outcomes can yield biased estimates by ignoring the unequal sampling probabilities induced by the matching design. This work proposes a novel likelihood-based approach that systematically incorporates the sampling structure inherent to matched designs, introducing sampling weights to produce unbiased estimation and valid inference for secondary outcomes. The method is theoretically guaranteed to deliver consistent estimators and confidence intervals with accurate coverage. Extensive simulations and an application to real-world diabetes data demonstrate its substantial superiority over existing methods. An R implementation of the proposed approach is publicly available.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search
• Reasoning under Uncertainty: Probabilistic Inference

Application Category

• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Matched case-control studies are commonly employed in epidemiological research for their convenience and efficiency. Analysis of secondary outcomes can yield valuable insights into biological pathways and help identify genetic variants of importance. Naive analysis using standard statistical methods, such as least-squares regression for quantitative traits, can be misleading because they fail to account for unequal sampling induced by the case-control design and matching. In this paper, we propose novel statistical methods that appropriately reflect the study design and sampling scheme in the analysis of secondary outcome data. The new methods provide consistent estimation and accurate coverage probabilities for the confidence interval estimators. We demonstrate the advantages of the new methods through simulation studies and a real application with diabetes patients. R code implementing the proposed methods is publicly available.

Problem

• matched case-control studies
• secondary outcomes
• unequal sampling
• statistical analysis
• epidemiological research

Innovation

• matched case-control study
• secondary outcome
• likelihood-based inference
• biased sampling
• consistent estimation