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Change Point Estimation in Monitoring Survival Time

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  • Hassan Assareh
  • Kerrie Mengersen

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.

Suggested Citation

  • Hassan Assareh & Kerrie Mengersen, 2012. "Change Point Estimation in Monitoring Survival Time," PLOS ONE, Public Library of Science, vol. 7(3), pages 1-10, March.
  • Handle: RePEc:plo:pone00:0033630
    DOI: 10.1371/journal.pone.0033630
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    References listed on IDEAS

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    1. O. Grigg & V. Farewell, 2004. "An overview of risk‐adjusted charts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 523-539, August.
    2. Grigg, Olivia & Spiegelhalter, David, 2007. "A Simple Risk-Adjusted Exponentially Weighted Moving Average," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 140-152, March.
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    Cited by:

    1. Athanasios Sachlas & Sotirios Bersimis & Stelios Psarakis, 2019. "Risk-Adjusted Control Charts: Theory, Methods, and Applications in Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 630-658, December.
    2. Lei Yong Lee & Michael Boon Chong Khoo & Sin Yin Teh & Ming Ha Lee, 2015. "A Variable Sampling Interval Synthetic Xbar Chart for the Process Mean," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.

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