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 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). Copyright © 2020 Samane Nematolahi et al.

Citation

Samane Nematolahi, Sahar Nazari, Zahra Shayan, Seyyed Mohammad Taghi Ayatollahi, Ali Amanati. Improved Kaplan-Meier Estimator in Survival Analysis Based on Partially Rank-Ordered Set Samples. Computational and mathematical methods in medicine. 2020;2020:7827434

Mesh Tags

PMID: 32587630

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