Perils of evidence-based medicine

Area of Science:

• Clinical Research Methodology
• Evidence-Based Medicine
• Biostatistics

Background:

• Random-assignment clinical trials (RCTs) are the cornerstone of evidence-based medicine.
• Heterogeneous patient populations in RCTs necessitate large sample sizes for statistical significance.
• Aggregate data from RCTs offer limited predictive value for individual patient responses.

Purpose of the Study:

• To highlight the limitations of traditional RCTs in predicting individual patient outcomes.
• To emphasize the necessity of identifying patient subgroups that benefit from specific treatments.
• To underscore the risks of ineffective or harmful treatments in patients not benefiting from interventions.

Main Methods:

• Critically evaluating the inferential capabilities of random-assignment clinical trials.
• Discussing the statistical challenges of generalizing findings from large patient samples to individuals.
• Examining the role and necessity of post-hoc analyses in clinical trial interpretation.

Main Results:

• Large sample sizes in RCTs do not guarantee individual patient predictability.
• The inference from RCTs is limited to the existence of a benefiting subset, not specific patient identification.
• Post-hoc analyses are crucial for uncovering treatment efficacy within specific patient subgroups.

Conclusions:

• Standard RCTs provide limited direct applicability to individual patient care due to population heterogeneity.
• Post-hoc subgroup analysis is essential for personalized medicine and optimizing treatment selection.
• Failure to identify benefiting subgroups can lead to widespread use of non-beneficial or harmful therapies.