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Change point estimation in monitoring survival time: average of posterior estimates of step change point model parameters

Queensland University of Technology
Assareh, Hassan ; Mengersen, Kerrie
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.4225/09/58575e538e8a3&rft.title=Change point estimation in monitoring survival time: average of posterior estimates of step change point model parameters&rft.identifier=10.4225/09/58575e538e8a3&rft.publisher=Queensland University of Technology&rft.description=The dataset was collected to model change point estimation in time-to-event data for a clinical process with dichotomous outcomes, death and survival, where patient mix was present. Modelling was completed using a Bayesian framework. The performance of the Bayesian estimators was investigated through simulation in conjunction with RAST CUSUM control charts for monitoring right censored survival time of patients who underwent cardiac surgery procedures within a follow-up period of 30 days. The dataset presents the average of posterior estimates (mode, sd.) of step change point model parameters ( and ) for a change in the mean survival time following signals (RL) from RAST CUSUM () where and . &rft.creator=Assareh, Hassan &rft.creator=Mengersen, Kerrie &rft.date=2012&rft.edition=1&rft.coverage=153.552920,-26.777500 152.452799,-26.777500 152.452799,-28.037280 153.552920,-28.037280 153.552920,-26.777500&rft_rights=Copyright: © 2012 Assareh, Mengersen.&rft_rights=Creative Commons Attribution 3.0 http://creativecommons.org/licenses/by/3.0/au/&rft_subject=Data processing&rft_subject=Death rates&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft_subject=Bayes theorem&rft_subject=Surgical and invasive medical procedures&rft_subject=Parameters &rft_subject=Monte Carlo method &rft_subject=Cusum &rft_subject=Cardiac surgery&rft_subject=Rast&rft_subject=MEDICAL AND HEALTH SCIENCES&rft_subject=Posterior&rft_subject=Estimates&rft_subject=Signals &rft_subject=Biotechnology&rft_subject=MATHEMATICAL SCIENCES&rft_subject=Markov models&rft.type=dataset&rft.language=English Access the data
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Open Licence view details
CC-BY

Creative Commons Attribution 3.0
http://creativecommons.org/licenses/by/3.0/au/

Copyright: © 2012 Assareh, Mengersen.

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Contact Information

Postal Address:
Distinguished Professor Kerrie Mengersen
Ph: +61 7 3138 2063

k.mengersen@qut.edu.au

Full description

The dataset was collected to model change point estimation in time-to-event data for a clinical process with dichotomous outcomes, death and survival, where patient mix was present. Modelling was completed using a Bayesian framework. The performance of the Bayesian estimators was investigated through simulation in conjunction with RAST CUSUM control charts for monitoring right censored survival time of patients who underwent cardiac surgery procedures within a follow-up period of 30 days.

The dataset presents the average of posterior estimates (mode, sd.) of step change point model parameters ( and ) for a change in the mean survival time following signals (RL) from RAST CUSUM () where and .

Data time period: 2010 to 2011

This dataset is part of a larger collection

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153.55292,-26.7775 152.4528,-26.7775 152.4528,-28.03728 153.55292,-28.03728 153.55292,-26.7775

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Subjects
Bayes theorem | Biotechnology | Cardiac surgery | Cusum | Data processing | Death rates | Estimates | Information and Computing Sciences | Mathematical Sciences | Medical and Health Sciences | Markov models | Monte Carlo method | Parameters | Posterior | Rast | Signals | Surgical and invasive medical procedures |

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