Big Data and Real-World Data based Cost-Effectiveness Studies and Decision-making Models: A Systematic Review and...

Area of Science:

• Health Economics
• Data Science
• Pharmacoeconomics

Background:

• Big data and real-world data (RWD) are increasingly utilized in cost-effectiveness analysis (CEA).
• However, the specific characteristics and methodologies of CEA studies employing big data and RWD are not well-understood.
• This study addresses this knowledge gap by reviewing existing literature.

Purpose of the Study:

• To review the characteristics and methodologies of CEA studies based on big data and RWD.
• To compare methodologies between CEA studies that use decision-analytic models and those that do not.

Main Methods:

• A comprehensive literature search was conducted across major databases (Medline, Embase, Web of Science, Cochrane Library) up to June 2020.
• Included were full CEA studies with incremental analysis using RWD for both effectiveness and costs.
• No restrictions were placed on publication date.

Main Results:

• 70 CEA studies using RWD were identified, with a significant increase in publications from 2011-2020.
• Few studies utilized big data; pharmacological interventions were most common.
• Studies with decision-analytic models focused on treatment regimens, while those without often evaluated pharmacological interventions.
• Effectiveness and costs were more frequently sourced from literature reviews in studies using models.
• All studies with models included sensitivity analyses, unlike some without models, which also lacked confounder control.

Conclusions:

• Real-world data (RWD) is becoming more prevalent in cost-effectiveness analysis (CEA).
• The application of big data in CEA remains limited.
• Future CEA studies using RWD should prioritize confounder control and long-term cost discounting when decision-analytic models are absent.