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On Cox proportional hazards model performance under different sampling schemes

Cox’s proportional hazards model (PH) is an acceptable model for survival data analysis. This work investigates PH models’ performance under different efficient sampling schemes for analyzing time to event data (survival data). We will compare a modified Extreme, and Double Extreme Ranked Set Sampli...

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Detalles Bibliográficos
Autores principales: Samawi, Hani, Yu, Lili, Yin, JingJing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132546/
https://www.ncbi.nlm.nih.gov/pubmed/37099503
http://dx.doi.org/10.1371/journal.pone.0278700
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author Samawi, Hani
Yu, Lili
Yin, JingJing
author_facet Samawi, Hani
Yu, Lili
Yin, JingJing
author_sort Samawi, Hani
collection PubMed
description Cox’s proportional hazards model (PH) is an acceptable model for survival data analysis. This work investigates PH models’ performance under different efficient sampling schemes for analyzing time to event data (survival data). We will compare a modified Extreme, and Double Extreme Ranked Set Sampling (ERSS, and DERSS) schemes with a simple random sampling scheme. Observations are assumed to be selected based on an easy-to-evaluate baseline available variable associated with the survival time. Through intensive simulations, we show that these modified approaches (ERSS and DERSS) provide more powerful testing procedures and more efficient estimates of hazard ratio than those based on simple random sampling (SRS). We also showed theoretically that Fisher’s information for DERSS is higher than that of ERSS, and ERSS is higher than SRS. We used the SEER Incidence Data for illustration. Our proposed methods are cost saving sampling schemes.
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spelling pubmed-101325462023-04-27 On Cox proportional hazards model performance under different sampling schemes Samawi, Hani Yu, Lili Yin, JingJing PLoS One Research Article Cox’s proportional hazards model (PH) is an acceptable model for survival data analysis. This work investigates PH models’ performance under different efficient sampling schemes for analyzing time to event data (survival data). We will compare a modified Extreme, and Double Extreme Ranked Set Sampling (ERSS, and DERSS) schemes with a simple random sampling scheme. Observations are assumed to be selected based on an easy-to-evaluate baseline available variable associated with the survival time. Through intensive simulations, we show that these modified approaches (ERSS and DERSS) provide more powerful testing procedures and more efficient estimates of hazard ratio than those based on simple random sampling (SRS). We also showed theoretically that Fisher’s information for DERSS is higher than that of ERSS, and ERSS is higher than SRS. We used the SEER Incidence Data for illustration. Our proposed methods are cost saving sampling schemes. Public Library of Science 2023-04-26 /pmc/articles/PMC10132546/ /pubmed/37099503 http://dx.doi.org/10.1371/journal.pone.0278700 Text en © 2023 Samawi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Samawi, Hani
Yu, Lili
Yin, JingJing
On Cox proportional hazards model performance under different sampling schemes
title On Cox proportional hazards model performance under different sampling schemes
title_full On Cox proportional hazards model performance under different sampling schemes
title_fullStr On Cox proportional hazards model performance under different sampling schemes
title_full_unstemmed On Cox proportional hazards model performance under different sampling schemes
title_short On Cox proportional hazards model performance under different sampling schemes
title_sort on cox proportional hazards model performance under different sampling schemes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10132546/
https://www.ncbi.nlm.nih.gov/pubmed/37099503
http://dx.doi.org/10.1371/journal.pone.0278700
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