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Change point estimation in monitoring survival time.

Assareh H, Mengersen K - PLoS ONE (2012)

Bottom Line: 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.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.These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

View Article: PubMed Central - PubMed

Affiliation: Discipline of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia.

ABSTRACT
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.

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Related in: MedlinePlus

Estimated probability of survival over all Parsonnet scores prior and after changes in the MST.Probabilities at the  and the  day of the follow-up period of 30 days prior and after (1) an increase of size , and (2) a decrease of size  in the MST. Prior and after the change are indexed by 1 and the value of .
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pone-0033630-g002: Estimated probability of survival over all Parsonnet scores prior and after changes in the MST.Probabilities at the and the day of the follow-up period of 30 days prior and after (1) an increase of size , and (2) a decrease of size in the MST. Prior and after the change are indexed by 1 and the value of .

Mentions: The effects of an increase of size and a drop of size in the MST on the probability of survival at the midpoint, day , and the end, day , of the follow-up period for all possible Parsonnet score are demonstrated in Figure 2. As expected, the probability of survival for each patient would increase when a jump in the MST occurred. However the magnitude of this increase is larger for patients with higher Parsonnet scores.


Change point estimation in monitoring survival time.

Assareh H, Mengersen K - PLoS ONE (2012)

Estimated probability of survival over all Parsonnet scores prior and after changes in the MST.Probabilities at the  and the  day of the follow-up period of 30 days prior and after (1) an increase of size , and (2) a decrease of size  in the MST. Prior and after the change are indexed by 1 and the value of .
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3306432&req=5

pone-0033630-g002: Estimated probability of survival over all Parsonnet scores prior and after changes in the MST.Probabilities at the and the day of the follow-up period of 30 days prior and after (1) an increase of size , and (2) a decrease of size in the MST. Prior and after the change are indexed by 1 and the value of .
Mentions: The effects of an increase of size and a drop of size in the MST on the probability of survival at the midpoint, day , and the end, day , of the follow-up period for all possible Parsonnet score are demonstrated in Figure 2. As expected, the probability of survival for each patient would increase when a jump in the MST occurred. However the magnitude of this increase is larger for patients with higher Parsonnet scores.

Bottom Line: 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.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.These superiorities are enhanced when probability quantification, flexibility and generalizability of the Bayesian change point detection model are also considered.

View Article: PubMed Central - PubMed

Affiliation: Discipline of Mathematical Sciences, Science and Engineering Faculty, Queensland University of Technology, Brisbane, Queensland, Australia.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus