Instrumental variable analysis in the presence of unmeasured confounding.

Area of Science:

• Epidemiology
• Biostatistics
• Health Informatics

Background:

• Observational studies, common with electronic health records, face bias from unmeasured confounding.
• Traditional methods struggle with unmeasured confounding, leading to inaccurate estimates.
• Instrumental variable (IV) analysis, established in econometrics, is gaining traction in clinical research to address this.

Discussion:

• This article reviews methods for implementing IV analysis for both binary and continuous outcomes.
• The focus is on practical application within clinical research settings.
• R code examples are provided to facilitate implementation.

Key Insights:

• IV analysis is a powerful tool to mitigate bias from unmeasured confounders in observational health data.
• The increasing availability of electronic health records necessitates robust methods like IV analysis.
• Practical implementation guidance and code are crucial for wider adoption.

Outlook:

• Further development and application of IV methods in clinical research are expected.
• Increased use of IV analysis can lead to more reliable findings from observational health data.
• This review aims to encourage broader adoption of IV techniques in pharmacoepidemiology and other clinical fields.