A mediator effect size in randomized clinical trials.

Area of Science:

• Biostatistics
• Clinical Trials
• Causal Inference

Background:

• Understanding treatment mechanisms requires identifying mediators linking treatments to outcomes.
• Quantifying the clinical importance of mediators is crucial for improving treatment efficacy in randomized clinical trials (RCTs).

Purpose of the Study:

• To develop a novel mediator effect size index for interpreting the clinical significance of mediators in RCTs.
• To provide a method for assessing the causal impact of mediators on treatment outcomes.

Main Methods:

• Derived a mediator effect size for linear models and generalized it for categorical mediators.
• Ensured the index is invariant to rescaling of mediator and outcome variables.
• Defined the index based on the difference between observed and maximal potential treatment effects.

Main Results:

• Introduced a new mediator effect size index applicable to various mediator types (categorical, ordered/non-ordered).
• The proposed index facilitates the interpretation of mediator's clinical importance in RCTs.
• Addressed complexities including multiple treatments, outcomes, mediators, and causal inference.

Conclusions:

• The developed mediator effect size index offers a valuable tool for causal inference in clinical trials.
• This approach enhances the understanding of treatment pathways and aids in optimizing treatment strategies.
• The study provides practical illustrations for applying the new index in real-world research.