Methodological considerations when analysing and interpreting real-world data.

Area of Science:

• Real-world data research
• Epidemiology
• Health services research

Background:

• Randomized trials are often unavailable for estimating treatment effects on clinical outcomes.
• Nonexperimental studies using large healthcare databases are crucial alternatives.
• Minimizing bias in these studies is essential for reliable results.

Purpose of the Study:

• To provide a non-technical overview of study design principles for real-world data research.
• To highlight critical design elements for minimizing bias in nonexperimental studies.
• To emphasize the importance of robust study design for accurate treatment effect estimation.

Main Methods:

• Utilizing state-of-the-art study designs for large healthcare databases (e.g., claims data, electronic health records).
• Implementing critical design elements: new-user designs, active comparators, and consideration of induction/latent periods.
• Employing propensity scores to balance covariates and control for measured confounding.
• Avoiding immortal-time bias through consistent definition of therapy initiation and follow-up.

Main Results:

• Effective study designs can significantly minimize bias in nonexperimental research.
• Specific design elements like new-user cohorts and propensity score methods improve reliability.
• Consistent definitions of treatment initiation and follow-up are vital for avoiding specific biases.

Conclusions:

• Robust study design is paramount for generating valid treatment effect estimates from real-world data.
• Adherence to best practices in nonexperimental study design can yield clinically meaningful outcomes.
• Careful consideration of design elements is necessary to ensure the integrity of real-world evidence.