Generated effect modifiers (GEM's) in randomized clinical trials.

Area of Science:

• Biostatistics
• Clinical Trials
• Precision Medicine

Background:

• Randomized clinical trials (RCTs) aim to estimate treatment effects and identify patient characteristics influencing outcomes.
• Effect modifiers, identified through non-zero interactions in regression models, are crucial for personalized treatment strategies in precision medicine.
• Multiple baseline predictors often exist, necessitating methods to effectively identify potential effect modifiers.

Purpose of the Study:

• To propose optimal methods for constructing composite variables that serve as effect modifiers in RCTs.
• To develop a composite variable as a linear combination of pre-treatment patient characteristics.
• To enhance the identification of patient-specific treatment effects for precision medicine.

Main Methods:

• Development of methods to construct a composite variable from pre-treatment patient characteristics.
• Utilizing linear combinations to generate a composite variable aimed at creating an effect modifier.
• Performance evaluation of proposed methods through simulation studies.

Main Results:

• The study proposes and evaluates methods for generating composite effect modifiers in RCTs.
• Simulation results assess the performance of different criteria for constructing these modifiers.
• An illustrative example from an actual RCT is provided.

Conclusions:

• The proposed methods offer a way to construct effective composite variables for identifying effect modifiers in RCTs.
• These composite effect modifiers can aid in optimizing treatment assignment for individual patients.
• The findings contribute to the advancement of precision medicine through better utilization of patient characteristics.