Research Paper#Survival Analysis, Ranked Set Sampling, Statistical Methods🔬 ResearchAnalyzed: Jan 3, 2026 19:46
Ranked Set Sampling for Survival Analysis: A Unified Framework
Published:Dec 27, 2025 17:15
•1 min read
•ArXiv
Analysis
This paper addresses a significant gap in survival analysis by developing a comprehensive framework for using Ranked Set Sampling (RSS). RSS is a cost-effective sampling technique that can improve precision. The paper extends existing RSS methods, which were primarily limited to Kaplan-Meier estimation, to include a broader range of survival analysis tools like log-rank tests and mean survival time summaries. This is crucial because it allows researchers to leverage the benefits of RSS in more complex survival analysis scenarios, particularly when dealing with imperfect ranking and censoring. The development of variance estimators and the provision of practical implementation details further enhance the paper's impact.
Key Takeaways
- •Develops a unified survival analysis framework for Ranked Set Sampling (RSS).
- •Extends RSS methods to include log-rank tests, weighted tests, and mean life functionals.
- •Addresses imperfect ranking and censoring in RSS.
- •Provides variance estimators and implementation details for practical use.
- •Demonstrates efficiency gains over simple random sampling (SRS).
Reference
“The paper formalizes Kaplan-Meier and Nelson-Aalen estimators for right-censored data under both perfect and concomitant-based imperfect ranking and establishes their large-sample properties.”