Comparative effectiveness research methodology using secondary data: A starting user's guide.

Area of Science:

• Health Services Research
• Biostatistics
• Epidemiology

Background:

• Secondary data, including claims and administrative databases, are increasingly utilized in comparative effectiveness research (CER).
• The growing reliance on secondary data necessitates robust methodologies for CER.
• Understanding the nuances of secondary data analysis is crucial for reliable research outcomes.

Purpose of the Study:

• To provide investigators using secondary data with insights into methodologies and statistical techniques for CER.
• To underscore the importance of rigorous planning in the early stages of CER investigations.
• To offer guidance on optimizing the quality and validity of CER studies that utilize secondary data.

Main Methods:

• Review of adjusted analyses and confounder control strategies.
• Explanation of propensity score analysis and instrumental variable methods.
• Discussion of risk prediction models (logistic, time-to-event), decision-curve analysis, and hypothesis testing interpretation.

Main Results:

• The review covers key statistical concepts essential for analyzing secondary data in CER.
• It highlights various analytical approaches, from basic adjustments to advanced modeling.
• Guidance is provided on interpreting statistical significance and model performance.

Conclusions:

• This review aims to improve the quality of CER conducted with secondary data.
• Investigators will gain a better understanding of statistical methods and the necessity of thorough study planning.
• The ultimate goal is to enhance the rigor and impact of research utilizing secondary data sources.