An IPCW Adjusted Win Statistics Approach in Clinical Trials Incorporating Equivalence Margins to Define Ties

Area of Science:

• Biostatistics
• Clinical Trial Methodology
• Survival Analysis

Background:

• Clinical trials often involve multiple endpoints to fully capture patient response.
• Existing methods may not adequately summarize differences across several time-to-event outcomes.
• Handling ties and censoring is crucial for accurate time-to-event analysis.

Purpose of the Study:

• To propose a new class of win statistics for time-to-event endpoints.
• To incorporate user-specified equivalence margins into win statistics.
• To develop robust estimation and inference procedures for these statistics under right censoring.

Main Methods:

• Development of win statistics for time-to-event data allowing for ties and equivalence margins.
• Application of inverse-probability-of-censoring weighting (IPCW) for handling right censoring.
• Conducting extensive simulations to evaluate the performance of the proposed methods.

Main Results:

• The proposed win statistics are identifiable and independent of the censoring distribution.
• Simulation studies demonstrate the reliability of the estimation and inference procedures.
• The methodology is illustrated using a real-world oncology clinical trial.

Conclusions:

• The novel win statistics provide a flexible and robust approach for analyzing multiple time-to-event endpoints in clinical trials.
• The proposed methods effectively address challenges posed by censoring and equivalence margins.
• This approach enhances the summary measures for between-group comparisons in survival data analysis.