Estimation of the treatment difference in multicenter trials

Area of Science:

• Clinical Trials Methodology
• Biostatistics
• Health Services Research

Background:

• Accurate estimation of treatment effects is crucial in multicenter clinical trials.
• Fixed effects estimators are commonly used but their performance can be affected by enrollment variations and missing data.
• Previous studies often simplified enrollment assumptions, potentially limiting real-world applicability.

Purpose of the Study:

• To compare the performance of three fixed effects estimators for treatment differences.
• To evaluate estimators under various random enrollment schemes and center-specific enrollment failures.
• To assess the impact of patient dropout and the number of centers on estimator performance.

Main Methods:

• Simulations were used to compare estimators based on expected mean squared errors.
• Five different random enrollment schemes were simulated.
• Poisson process enrollment was assumed within centers, with constant or gamma-distributed mean rates.

Main Results:

• The simpler fixed effects estimator often outperformed others across various scenarios.
• This held true even when data generation models were more complex than the estimator's assumed model.
• The impact of centers failing to enroll patients was explicitly considered.

Conclusions:

• Simpler fixed effects models can be robust and efficient in multicenter trials.
• Accounting for realistic enrollment challenges, including center non-enrollment, is important for accurate treatment effect estimation.
• The choice of estimator should consider the balance between model complexity and practical performance.