Evaluating treatment effectiveness: Complementing RCTs with real-world data.

Area of Science:

• Health services research
• Data science in healthcare
• Comparative effectiveness research

Background:

• Randomized controlled trials (RCTs) are the benchmark for treatment efficacy.
• Real-world data (RWD) provides insights into treatment effectiveness in diverse populations.
• The increasing use of RWD necessitates robust quality assessment.

Purpose of the Study:

• To explore the importance of fit-for-purpose data in RWD studies.
• To emphasize the need for rigorous evaluation of RWD quality.
• To support valid and actionable evidence generation from RWD.

Main Methods:

• The commentary discusses the critical role of data quality dimensions.
• Illustrative examples from COVID-19 comparative effectiveness research are utilized.
• Focuses on the assessment of relevance, extensiveness, timeliness, coherence, and reliability.

Main Results:

• The utility of RWD studies is contingent upon the fitness-for-purpose of the data.
• Careful assessment of five key quality dimensions is required.
• High data quality and appropriate study design are critical for RWD credibility.

Conclusions:

• Ensuring RWD quality is vital for post-pandemic healthcare decision-making.
• Proper evaluation of RWD sources enhances credibility.
• Findings support the integration of RWD into meta-analyses, guidelines, and policy.