Evaluating methodological assumptions in comparative effectiveness research: overcoming pitfalls.

Area of Science:

• Health Services Research
• Biostatistics
• Clinical Epidemiology

Background:

• Comparative Effectiveness Research (CER) employs sophisticated statistical methods.
• Commonly used CER techniques rely on significant, often untestable, assumptions regarding data distribution, heterogeneity, and populations.
• The validity of CER findings is contingent upon the accuracy of these underlying assumptions.

Purpose of the Study:

• To identify and explain potential challenges and limitations in widely adopted CER methodologies.
• To discuss the impact of data quality and database heterogeneity on CER tool performance.
• To underscore the necessity for standardized practices in CER.

Main Methods:

• Review of common statistical techniques used in CER.
• Analysis of assumptions inherent in network meta-analysis, observational data analysis, and patient-reported outcome evaluation.
• Discussion of data quality and heterogeneity issues impacting CER.

Main Results:

• Common CER methods, including those for network meta-analysis and observational data, are susceptible to pitfalls due to untestable assumptions.
• Data quality and database heterogeneity significantly affect the performance of standard CER tools.
• Existing CER approaches may yield results whose validity is compromised by underlying procedural assumptions.

Conclusions:

• Researchers must be aware of the limitations and potential biases associated with standard CER statistical tools.
• Improving data quality and addressing database heterogeneity are crucial for enhancing the reliability of CER findings.
• The development and implementation of standardized CER procedures are essential for advancing the field.