[Evaluating the real-world vaccine effectiveness using a regression discontinuity design].

Area of Science:

• Vaccinology
• Epidemiology
• Biostatistics

Background:

• Post-marketing evaluation of vaccine effectiveness is crucial for public health.
• Randomized controlled trials (RCTs) face cost, ethical, and logistical challenges.
• Observational data offers a valuable alternative for real-world effectiveness studies.

Purpose of the Study:

• To describe the application of regression discontinuity design (RDD) for vaccine effectiveness estimation.
• To highlight RDD's suitability for interventions with age-based eligibility cutoffs.
• To demonstrate RDD as a tool for robust causal inference in vaccine studies.

Main Methods:

• Utilizes observational data with a defined eligibility cutoff (e.g., age).
• Employs regression discontinuity design (RDD) to estimate intervention effects.
• Compares outcomes for individuals just above and below the eligibility threshold.

Main Results:

• RDD provides a higher level of causal inference evidence compared to other observational methods.
• This method yields more realistic estimates of vaccine effectiveness in real-world settings.
• RDD overcomes practical and ethical limitations inherent in randomized controlled trials.

Conclusions:

• Regression discontinuity design is an essential tool for post-marketing vaccine evaluation.
• RDD enables accurate quantification of vaccine effects, enhancing understanding of public health interventions.
• This methodology supports evidence-based decision-making in vaccination programs.