The analysis of multiple endpoints in clinical trials

Area of Science:

• Biostatistics
• Clinical Trials
• Statistical Inference

Background:

• Randomized clinical trials frequently employ multiple endpoints, increasing the risk of Type I errors with conventional significance testing.
• Selecting a single primary endpoint is not always feasible, necessitating alternative methods for robust statistical inference.

Purpose of the Study:

• To evaluate methods for handling multiple endpoints in clinical trials to control Type I error rates.
• To investigate the conservatism of Bonferroni correction for correlated endpoints.
• To propose and explore a global test statistic for simultaneous inference across various endpoint types.

Main Methods:

• Examined the conservatism of Bonferroni correction under multivariate normal distributions.
• Developed a global test statistic framework applicable to asymptotically normal test statistics.
• Considered quantitative, binary, and survival endpoints within the proposed framework.

Main Results:

• Bonferroni correction can be overly conservative, especially with correlated endpoints.
• The proposed global test statistic offers a unified approach for multiple endpoints.
• The framework accommodates diverse data types, including quantitative, binary, and survival data.

Conclusions:

• Global test statistics provide a more appropriate method for statistical inference with multiple endpoints compared to Bonferroni correction.
• The developed framework offers a flexible and powerful tool for analyzing complex clinical trial data.
• Careful consideration of endpoint multiplicity is crucial for accurate clinical trial interpretation.