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Title: Modelling optimal use of tests for monitoring disease progression and recurrence
Author: Sitch, Alice Jayne
ISNI:       0000 0004 8506 0773
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2019
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Background: Monitoring to identify disease recurrence or progression is common, often with limited evidence to support the tests used, subsequent decisions, frequency and duration of monitoring. Aims: To develop methods for designing evidence-based monitoring strategies and estimating measurement error, a key consideration in selecting monitoring tests. Methods: To investigate studies of measurement error: frameworks were identified; design, analysis and reporting of studies were reviewed; a case study was analysed; and, simulation studies were performed to evaluate varying sample size and outlier detection methods. To develop methods for designing monitoring strategies the methods literature was reviewed and simulation models were developed and validated. Results: Biological variability studies are often poorly designed and reported. Studies are frequently small and may not produce valid results; the required precision of estimates can inform the sample size. Outlier detection can negatively bias variability estimates; methods should be used with caution, with interpretation allowing for potential bias. Modelling monitoring data requires knowledge of the natural history of disease, test performance and measurement error; such evaluation enables selection of evidence-based monitoring strategies prior to full-scale investigation. Conclusions: Poor monitoring tests can be identified early using small-scale studies and monitoring strategies should be optimised prior to full evaluation.
Supervisor: Not available Sponsor: NIHR
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: R Medicine (General)