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Title: Risk-adjusted monitoring and smoothing in medical contexts
Author: Grigg, O. A.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2004
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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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available