A problem for achieving informed choice.

Area of Science:

• Medical Statistics
• Decision Theory
• Clinical Trial Analysis

Background:

• Informed choice and consent are fundamental in healthcare.
• Current clinical trials predominantly use classical statistical methods.
• Classical statistics present challenges in communicating probabilistic information effectively.

Purpose of the Study:

• To argue that classical statistics are inadequate for informing patient choice.
• To highlight the limitations of classical probability in therapeutic decision-making.
• To advocate for statistical approaches that provide probabilities for therapy benefits and harms.

Main Methods:

• Critique of classical statistical inference in the context of informed choice.
• Explanation of the difficulties in communicating classical statistical probabilities.
• Introduction of Bayesian statistical inference as a superior alternative.

Main Results:

• Classical statistics do not provide the optimal information for informed patient decisions.
• The probabilistic framework of classical statistics is complex and hard to convey.
• Bayesian methods are better suited to assign probabilities to hypotheses about treatment effects.

Conclusions:

• Bayesian statistical inference is more appropriate for informing therapeutic choices.
• A shift towards Bayesian approaches can improve patient understanding and decision-making.
• Effective communication of statistical evidence is crucial for informed consent and choice.