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Updated: Jun 3, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Nonparametric estimation of a biometric function using neoteric ranked set sampling with application to breast cancer

Leila Jabari Koopaei^1,2, Ehsan Zamanzade^1,3, Afshin Parvardeh¹

¹Department of Statistics, Faculty of Mathematics and Statistics, University of Isfahan, Isfahan, Iran.

Journal of Biopharmaceutical Statistics

|June 1, 2026

Summary

Neoteric Ranked Set Sampling (NRSS) improves the estimation of the mean residual life (MRL) function. This new method is more efficient than traditional sampling for survival analysis, especially with smaller sample sizes.

Keywords:

Mean residual life Monte Carlo simulation breast cancer neoteric ranked set sampling nonparametric estimation

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Optimization of Breast Biopsy and Mastectomy Sample Collection Procedures for Biobanking, Personalized Medicine, and Research Applications

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Published on: September 2, 2025

Area of Science:

Statistics
Survival Analysis
Biostatistics

Background:

The mean residual life (MRL) function is crucial for interpreting survival data, particularly for patients.
Ranked set sampling (RSS) offers an efficient alternative to simple random sampling (SRS) in health studies where data collection is costly or time-consuming.
Modifications to RSS aim to further enhance its efficiency in statistical estimations.

Purpose of the Study:

To investigate the estimation of the MRL function using Neoteric Ranked Set Sampling (NRSS).
To compare the efficiency of the NRSS estimator against RSS and SRS estimators.
To apply the NRSS method to real-world survival data for cancer patients.

Main Methods:

Utilizing Neoteric Ranked Set Sampling (NRSS) for MRL function estimation.
Conducting Monte Carlo simulations to compare NRSS with RSS and SRS.
Applying the NRSS procedure to survival time data from the SEER program for breast cancer patients.

Main Results:

The NRSS estimator demonstrated superior efficiency compared to RSS and SRS estimators, particularly for small to moderate sample sizes.
The application to SEER data confirmed that NRSS enhances the MRL estimation process.
The study indicated that NRSS allows for achieving a predetermined level of precision with a smaller sample size.

Conclusions:

Neoteric Ranked Set Sampling (NRSS) provides a more efficient approach for estimating the mean residual life (MRL) function in survival analysis.
The NRSS method is advantageous in health research settings, potentially reducing the required sample size.
This technique offers a valuable tool for analyzing complex survival data, such as that from cancer registries.