Change point estimation in monitoring survival time.

Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in...

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Autores principales:	Hassan Assareh, Kerrie Mengersen
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/a4d64ccdb7794daca36fb2cca6ef4c29
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spelling	oai:doaj.org-article:a4d64ccdb7794daca36fb2cca6ef4c292021-11-18T07:24:56ZChange point estimation in monitoring survival time.1932-620310.1371/journal.pone.0033630https://doaj.org/article/a4d64ccdb7794daca36fb2cca6ef4c292012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22438969/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.Hassan AssarehKerrie MengersenPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 3, p e33630 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hassan Assareh Kerrie Mengersen Change point estimation in monitoring survival time.
description	Precise identification of the time when a change in a hospital outcome has occurred enables clinical experts to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for survival time of a clinical procedure in the presence of patient mix in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step change in the mean survival time of patients who underwent cardiac surgery. The data are right censored since the monitoring is conducted over a limited follow-up period. We capture the effect of risk factors prior to the surgery using a Weibull accelerated failure time regression model. Markov Chain Monte Carlo is used to obtain posterior distributions of the change point parameters including location and magnitude of changes and also corresponding probabilistic intervals and inferences. The performance of the Bayesian estimator is investigated through simulations and the result shows that precise estimates can be obtained when they are used in conjunction with the risk-adjusted survival time CUSUM control charts for different magnitude scenarios. The proposed estimator shows a better performance where a longer follow-up period, censoring time, is applied. In comparison with the alternative built-in CUSUM estimator, more accurate and precise estimates are obtained by the Bayesian estimator. These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.
format	article
author	Hassan Assareh Kerrie Mengersen
author_facet	Hassan Assareh Kerrie Mengersen
author_sort	Hassan Assareh
title	Change point estimation in monitoring survival time.
title_short	Change point estimation in monitoring survival time.
title_full	Change point estimation in monitoring survival time.
title_fullStr	Change point estimation in monitoring survival time.
title_full_unstemmed	Change point estimation in monitoring survival time.
title_sort	change point estimation in monitoring survival time.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/a4d64ccdb7794daca36fb2cca6ef4c29
work_keys_str_mv	AT hassanassareh changepointestimationinmonitoringsurvivaltime AT kerriemengersen changepointestimationinmonitoringsurvivaltime
_version_	1718423492079124480

Change point estimation in monitoring survival time.

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