Realism in evidence based medicine: interpreting the randomised controlled trial

Area of Science:

• Philosophy of Science
• Social and Natural Sciences Research Methodology

Background:

• The dominant empiricist paradigm in scientific research often leads to misinterpretations of complex data.
• Randomized controlled trials (RCTs) are widely used but can be subject to flawed interpretations.
• A need exists for a more nuanced framework to understand scientific observations.

Purpose of the Study:

• To elaborate a realist framework for scientific explanation.
• To apply this realist framework to analyze observations from randomized controlled trials.
• To demonstrate how realism can rectify common misinterpretations within the empiricist paradigm.

Main Methods:

• Development of a realist philosophical framework.
• Application of realist concepts (mechanism and context) to analyze four types of situations generating RCT observations.
• Comparative analysis of realist and empiricist interpretations of trial data.

Main Results:

• The realist framework, utilizing 'mechanism' and 'context,' effectively addresses several misinterpretations of randomized controlled trials.
• Realism provides a more adequate theory of causation compared to dominant empiricist approaches.
• Statistical findings from trials are shown to be subsidiary to underlying causal mechanisms and contexts.

Conclusions:

• Realism offers a superior paradigm for scientific research and explanation, particularly in interpreting experimental data.
• Evidence-based medicine should integrate realist principles to temper empiricism and improve causal reasoning.
• Adopting realism necessitates a re-evaluation of the role of statistical significance in favor of understanding causal processes.