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).
Highlights
The idea of ranked set sampling (RSS) was introduced by McIntyre [1] for the first time
Rank-based sampling designs such as Partially Rank-Ordered Set (PROS) can help overcome this difficulty by ranking a small number of sampling units based on a concomitant variable
We considered the problem of the KM estimator that is a proper and commonly used technique in survival analysis associated with an imperfect PROS sampling design
Summary
The idea of ranked set sampling (RSS) was introduced by McIntyre [1] for the first time It can provide a more structural method for collecting the sample units. Both sampling methods are similar with a clear difference; in the PROS sampling design that we use in this paper, the ranker divides the sampling units into ranked subsets of prespecified sizes based on their partial ranks [2] These sampling designs are techniques to obtain more representative samples from the underlying population where measurement of the units is costly and/or time-consuming. Mahdizadeh and StrzalkowskaKominiak [16] have proposed a confidence interval for a distribution function when data are right-censored with random censoring time by applying RSS design.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Computational and Mathematical Methods in Medicine
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
