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Nonparametric Two-Sample Tests of the Marginal Mark Distribution with Censored Marks
1Department of Biostatistics and Computational Biology, University of Rochester.
Summary
This study introduces new statistical tests for analyzing clinical trial data with marked endpoints, addressing issues of induced censoring. The proposed rank-based tests demonstrate superior power, especially with heavy-tailed data, improving the analysis of failure events.
Area of Science:
- Biostatistics
- Clinical Trials
- Survival Analysis
Background:
- Clinical studies sometimes collect auxiliary marks at failure events.
- These marked endpoints can be subject to induced censoring due to follow-up limitations.
Purpose of the Study:
- To propose novel two-sample tests for comparing mark-scale distributions.
- To develop tests that accommodate arbitrary associations between marks and time.
Main Methods:
- Introduced two new families of statistical tests.
- One family extends existing semi-parametric linear tests nonparametrically.
- The second family is based on novel marked rank processes.
Main Results:
- Proposed tests maintain desired statistical size.
- Tests show adequate power for detecting changes in marginal mark distribution.
- Rank-based tests are nearly twice as powerful as linear tests for heavy-tailed distributions.
Conclusions:
- The novel tests effectively handle induced censoring in marked endpoints.
- Rank-based methods offer significant advantages in statistical power for specific data types.
- These tests enhance the analysis of clinical trial outcomes with auxiliary mark data.
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