Improved Kaplan-Meier Estimator in Survival Analysis Based on Partially Rank-Ordered Set Samples.
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2020;
2020:7827434. [PMID:
32587630 PMCID:
PMC7296468 DOI:
10.1155/2020/7827434]
[Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 04/03/2020] [Accepted: 04/25/2020] [Indexed: 11/17/2022]
Abstract
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).
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