Risk-adjusted monitoring and smoothing in medical contexts

Statistical process control methods were originally implemented in the industrial context. With increasing interest in the measurement and comparison of health outcomes, quality control tools are now being applied to medical data. However, outcomes measured on patients may have greatly differing ass...

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Main Author: Grigg, O. A.
Published: University of Cambridge 2004
Subjects:
Online Access:http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599741
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spelling ndltd-bl.uk-oai-ethos.bl.uk-5997412015-03-20T06:00:04ZRisk-adjusted monitoring and smoothing in medical contextsGrigg, O. A.2004Statistical process control methods were originally implemented in the industrial context. With increasing interest in the measurement and comparison of health outcomes, quality control tools are now being applied to medical data. However, outcomes measured on patients may have greatly differing associated risks, making standard quality control tools often inappropriate. Nevertheless, if patient risk an be adequately explained by a set of measurable patient covariates, specially developed statistical monitoring tools can be employed that take the risk into account. A comprehensive discussion of risk-adjusted quality control charts and methods is given, the theoretical form of existing and developed methods being described, as well as issues concerning considerations of design and enhancements to the methods. With a focus on discrete data types and particular case-mix structures, the charts are compared under various optimality criteria and applied to some example datasets. Multivariate risk-adjusted charts are also discussed in depth and the particular problem of parallel variables addressed via an example. Estimation of the level of a process throughout monitoring, and, most importantly, following signal of a chart, is of especial interest here. The exponentially weighted moving average (EWMA) chart is the chart seeming to be most suited to the estimation of level, but use of the EWMA as a monitoring tool is thought to be more approachable from a Bayesian standpoint. The Bayesian origin of the EWMA as an estimator, or smoother, of process level is described in detail. Similar Bayesian models are also described and related to the EWMA. Based upon the discussed models, a possible Bayesian monitoring scheme that produces an and estimate of process level as a by-product is developed and a demonstration of its application given.610.21University of Cambridgehttp://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599741Electronic Thesis or Dissertation
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topic 610.21
spellingShingle 610.21
Grigg, O. A.
Risk-adjusted monitoring and smoothing in medical contexts
description Statistical process control methods were originally implemented in the industrial context. With increasing interest in the measurement and comparison of health outcomes, quality control tools are now being applied to medical data. However, outcomes measured on patients may have greatly differing associated risks, making standard quality control tools often inappropriate. Nevertheless, if patient risk an be adequately explained by a set of measurable patient covariates, specially developed statistical monitoring tools can be employed that take the risk into account. A comprehensive discussion of risk-adjusted quality control charts and methods is given, the theoretical form of existing and developed methods being described, as well as issues concerning considerations of design and enhancements to the methods. With a focus on discrete data types and particular case-mix structures, the charts are compared under various optimality criteria and applied to some example datasets. Multivariate risk-adjusted charts are also discussed in depth and the particular problem of parallel variables addressed via an example. Estimation of the level of a process throughout monitoring, and, most importantly, following signal of a chart, is of especial interest here. The exponentially weighted moving average (EWMA) chart is the chart seeming to be most suited to the estimation of level, but use of the EWMA as a monitoring tool is thought to be more approachable from a Bayesian standpoint. The Bayesian origin of the EWMA as an estimator, or smoother, of process level is described in detail. Similar Bayesian models are also described and related to the EWMA. Based upon the discussed models, a possible Bayesian monitoring scheme that produces an and estimate of process level as a by-product is developed and a demonstration of its application given.
author Grigg, O. A.
author_facet Grigg, O. A.
author_sort Grigg, O. A.
title Risk-adjusted monitoring and smoothing in medical contexts
title_short Risk-adjusted monitoring and smoothing in medical contexts
title_full Risk-adjusted monitoring and smoothing in medical contexts
title_fullStr Risk-adjusted monitoring and smoothing in medical contexts
title_full_unstemmed Risk-adjusted monitoring and smoothing in medical contexts
title_sort risk-adjusted monitoring and smoothing in medical contexts
publisher University of Cambridge
publishDate 2004
url http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.599741
work_keys_str_mv AT griggoa riskadjustedmonitoringandsmoothinginmedicalcontexts
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