Evaluating Treatment Efficacy by Combining Multiple Measures in Clinical Trial Applications
Abdullah Al Masud1, Samaradasa Weerahandi2, Ching-Ray Yu3
1Biostatistics and Programming, Sanofi US, 55 Corporate Dr, Bridgewater, NJ, 08807, USA. abdullah.masud@sanofi.com.
Pharmaceutical Medicine
|December 1, 2022
Summary
This study introduces a novel, efficient method for analyzing composite endpoints in clinical trials using pairwise comparisons. The proposed statistical tests simplify complex data analysis, reducing computational burden for treatment effect evaluation.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Statistical Analysis
Background:
- Clinical trials assess new treatments against standard care using various measures.
- Composite endpoints combine multiple individual outcomes into a single primary outcome for analysis.
- Existing methods for composite endpoint analysis can be computationally intensive and complex.
Purpose of the Study:
- To propose an efficient analysis method for composite endpoints in clinical trials.
- To introduce a statistical testing procedure for hierarchical composite scores.
- To evaluate treatment effects using a novel pairwise comparison approach.
Main Methods:
- Utilized Gehan's (1965) ranking approach for pairwise comparison between treatment and control groups.
- Developed a subject-level pairwise composite score to reduce computational complexity.
- Proposed two statistical tests: a parametric test with asymptotic F-distribution and a non-parametric bootstrap procedure.
Main Results:
- The proposed pairwise approach significantly reduces computational time and complexity.
- Simulation studies assessed the operating characteristics of the new methods.
- Methods were illustrated using publicly available clinical study data.
Conclusions:
- The novel analysis method provides an efficient and statistically sound approach for composite endpoints.
- The proposed parametric and bootstrap tests are effective for evaluating treatment effects.
- This approach simplifies complex data analysis in clinical trials.
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