On the Kaplan–Meier estimator based on ranked set samples

Strzałkowska-Kominiak, Ewa; Mahdizadeh, M.

doi:10.1080/00949655.2013.794348

Cited by 16 publications

(6 citation statements)

References 8 publications

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“…Because RSS allows us to achieve a precision level with a smaller sample size as compared with SRS. Nonparametric inference in RSS about the population mean, 2,3 and distribution function [4][5][6][7] has been studied recently. Some two-sample problems in this design have also been investigated.…”

Section: Introductionmentioning

confidence: 99%

On estimating the area under the ROC curve in ranked set sampling

Mahdizadeh

Zamanzade

2022

Stat Methods Med Res

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In medical research, the receiver operating characteristic curve is widely used to evaluate accuracy of a continuous biomarker. The area under this curve is known as an index for overall performance of the biomarker. This article develops three new estimators of the area under the receiver operating characteristic curve in ranked set sampling. The first estimator is obtained under normality assumption. The two other estimators are constructed by applying a Box–Cox transformation on data, and then using either a parametric estimator or a kernel-density-based estimator. A simulation study is carried out to compare the proposed estimators with those available in the literature. It emerges that the new estimators offer some advantages in specific situations. Application of the methods is demonstrated using real data in the context of medicine.

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Section: Introductionmentioning

confidence: 99%

On estimating the area under the ROC curve in ranked set sampling

Mahdizadeh

Zamanzade

2022

Stat Methods Med Res

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“…Bouza-Herrera and Al-Omari [3] discuss some new developments in this area. Some applications include auditing [8], environmental studies [9,18], cluster randomized designs [19], and medicine [15].…”

Section: Introductionmentioning

confidence: 99%

CDF estimation in multistage pair ranked set sampling

Mahdizadeh

Zamanzade

2022

Hacettepe Journal of Mathematics and Statistics

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Multistage pair ranked set sampling (MSPRSS) is a rank-based design that improves statistical inference with respect to simple random sampling of the same size. It is applicable when exact measurement is difficult, but judgment raking of the potential sample units can be done fairly accurately and easily. The ranking is usually performed based on personal judgment or a concomitant variable, and need not be totally free of errors. This article deals with estimating the cumulative distribution function in MSPRSS. The proposed estimator is theoretically compared with its contenders in the literature. The findings are supported by numerical evidence from simulation, and real data in the context of body fat analysis. Finally, a cost analysis is performed to show the advantage of the estimator.

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“…However, survival analyses are expensive due to the need of a large sample size and the potentially long follow-up duration [12]. For the sake of parsimony, we may consider the cost-effective sampling methods, in which only a small proportion of the available units is measured; however, they contain a portion of the information contributed by all of the units; for more information, see [13].…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al [15] have used RSS for estimating the KM estimator of a reliability function with random right-censored data where the population distribution is unknown. Strzalkowska-Kominiak and Mahdizadeh [13] have proposed a KM estimator based on RSS when censored data are under random detection limit assumption. Mahdizadeh and Strzalkowska-Kominiak [16] have proposed a confidence interval for a distribution function when data are right-censored with random censoring time by applying RSS design.…”

Section: Introductionmentioning

confidence: 99%

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

Nematolahi

Nazari

Shayan

et al. 2020

Computational and Mathematical Methods in Medicine

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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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On the Kaplan–Meier estimator based on ranked set samples

Cited by 16 publications

References 8 publications

On estimating the area under the ROC curve in ranked set sampling

On estimating the area under the ROC curve in ranked set sampling

CDF estimation in multistage pair ranked set sampling

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

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