2019-08-15T13:58:24 n10853

Change point estimation in monitoring survival time following cardiac surgery

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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 our 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.

Geographical area of data collection

153.552920,-26.777500 152.452799,-26.777500 152.452799,-28.037280 153.552920,-28.037280 153.552920,-26.777500


Assareh, Hassan & Mengersen, Kerrie (2012) Change Point Estimation in Monitoring Survival Time. PLoS ONE, 7(3), http://dx.doi.org/10.1371/journal.pone.0033630

Research areas

Death rates
Monte Carlo method
Cardiac surgery
Data processing
Information and computing sciences
Surgical and invasive medical procedures
Bayes theorem
Markov models

Cite this collection

Mengersen, Kerrie; Assareh, Hassan (2014): Change point estimation in monitoring survival time following cardiac surgery . Queensland University of Technology. (Dataset) https://doi.org/10.4225/09/5857557c3122c

Related information

Hassan Assareh, former research officer, QUT - collaborator http://eprints.qut.edu.au/view/person/Assareh,_Hassan.html

Access the data

Data file types

not stated


Creative Commons Attribution 4.0 (CC-BY)


Copyright: © 2012 Assareh, Mengersen.

Dates of data collection

From 2012-01-01 to 2012-12-31


Was collected by
Kerrie Mengersen  (Researcher)


Name: Distinguished Professor Kerrie Mengersen
Phone: +61 7 3138 2063


Date record created:
Date record modified:
Record status:
Published - Open Access