Improved Kaplan-Meier Estimator in Survival Analysis Based on Partially Rank-Ordered Set Samples

This study presents a novel methodology to investigate the nonparametric estimation of a survival probability under random censoring time using the ranked observations from a Partially Rank-Ordered Set (PROS) sampling design and employs it in a hematological disorder study. The PROS sampling design...

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Bibliographic Details
Published in:Computational and mathematical methods in medicine 2020, Vol.2020 (2020), p.1-11
Main Authors: Ayatollahi, Seyyed Mohammad Taghi, Shayan, Zahra, Nazari, Sahar, Nematolahi, Samane, Amanati, Ali
Format: Article
Language:English
Subjects:
Algorithms
Child
Computational Biology
Computer Simulation
Databases, Factual - statistics & numerical data
Hematologic Neoplasms - mortality
Humans
Kaplan-Meier Estimate
Mathematical Concepts
Models, Statistical
Probability
Sampling Studies
Statistics, Nonparametric
Survival Analysis
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Summary:This study presents a novel methodology to investigate the nonparametric estimation of a survival probability under random censoring time using the ranked observations from a Partially Rank-Ordered Set (PROS) sampling design and employs it in a hematological disorder study. The PROS sampling design has numerous applications in medicine, social sciences and ecology where the exact measurement of the sampling units is costly; however, sampling units can be ordered by using judgment ranking or available concomitant information. The general estimation methods are not directly applicable to the case where samples are from rank-based sampling designs, because the sampling units do not meet the identically distributed assumption. We derive asymptotic distribution of a Kaplan-Meier (KM) estimator under PROS sampling design. Finally, we compare the performance of the suggested estimators via several simulation studies and apply the proposed methods to a real data set. The results show that the proposed estimator under rank-based sampling designs outperforms its counterpart in a simple random sample (SRS).
ISSN:1748-670X
1748-6718
DOI:10.1155/2020/7827434