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Published on: January 16, 2019
Clinical trials and causation: Bayesian perspectives
1George Washington University, Washington, DC 20052.
Statistics in Medicine
|August 1, 1993
Summary
Establishing causal efficacy of treatments requires clear causation concepts. This study examines clinical trial causation, contrasting manipulability and epidemiological approaches, and their links to complex causality frameworks.
Area of Science:
- Philosophy of Science
- Epidemiology
- Biostatistics
- Clinical Trials
Background:
- Clinical trials are considered the gold standard for establishing treatment efficacy and safety.
- The underlying concept of causation in clinical trials is often assumed to be straightforward but warrants deeper examination.
- Existing statistical and regulatory views on causation in clinical trials may overlook crucial nuances.
Purpose of the Study:
- To critically evaluate the concept of causation as applied in clinical trials.
- To compare and contrast the manipulability approach to causation with epidemiological causation.
- To explore the connections between these causal concepts and advanced frameworks like directed graphical causal modeling.
Main Methods:
- Discussion of the manipulability (counterfactual) approach to causation.
- Characterization of 'epidemiological causation' as probabilistic and population-level.
- Analysis of relationships between different causal concepts and frameworks (Cartwright, Rubin, Holland, Glymour).
Main Results:
- The concept of causation in clinical trials is not as clear as commonly assumed.
- The manipulability approach, while useful in physiology, has unclear links to population-level epidemiological causation.
- Epidemiological causation is probabilistic, population-dependent, and relies on specific criteria and study designs.
Conclusions:
- A clearer understanding of causation is essential for interpreting clinical trial results accurately.
- Integrating different perspectives on causation can enhance the rigor of epidemiological and clinical research.
- Further clarification is needed to bridge the gap between individual-level and population-level causal inference.
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