A Selective Review of Negative Control Methods in Epidemiology

Area of Science:

• Epidemiology
• Causal Inference
• Health Data Science

Background:

• Bias is a significant challenge in epidemiological research.
• Negative controls offer a robust method for bias detection and adjustment.
• Existing methods for bias correction have limitations.

Purpose of the Study:

• To introduce negative controls to a wider audience.
• To provide guidance on the principled design and causal analysis of negative controls.
• To formalize a negative control framework for epidemiological studies.

Main Methods:

• Review of causal and statistical assumptions for negative control analyses.
• Summary of practical strategies and validation criteria for negative control implementation.
• Exploration of statistical methodologies for confounding bias detection, reduction, and correction.

Main Results:

• Negative controls can be effectively combined with subject-matter knowledge.
• Existing statistical methodologies address confounding bias.
• Recent advances include nonparametric identification of causal effects using double-negative control designs.

Conclusions:

• Healthcare data routinely contains negative controls, offering potential for valid causal inference.
• Negative controls are an emerging area of interest in health and social sciences.
• Wider dissemination of these novel methods is needed for adoption by epidemiologists.