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Title: Statistical methods for studying the frequency of monitoring chronic conditions
Author: Oke, Jason Lee
ISNI:       0000 0004 5362 1476
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2014
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Evidence for the timing and frequency of monitoring chronic conditions in primary care is often of low quality or absent. Recently, a methodology based upon repeated measures of monitoring tests has been developed to evaluate monitoring strategies which differ by frequency and the length of interval. In studies using this approach, it has been shown that there is potential for substantial over-detection and over-treatment through routine monitoring. The aim of this thesis was to extend the existing methodology to outcomes with highly skewed distributions and tests that are interpreted as dichotomous or categorical states. These methods were applied to find evidence for the intervals of monitoring for microalbuminuria in people with type 1 diabetes and screening for diabetic retinopathy. Statistical models were used to describe the progression of albuminuria and retinopathy and estimate the rates of false positive and false negative tests over time. Simulation was then used to create hypothetical cohorts of people being monitored and screening at different frequencies and for different baseline risk. The impact of changing the timing of tests within the programme was measured and evaluated. The basic framework of the methodology can be usefully extended to tests that have categorical outcome or highly skewed distributions. More frequent monitoring of microalbuminuria in people with type 1 diabetes, and more frequent screening of diabetic retinopathy leads to disproportionate numbers of false positive results and potential over-treatment or referral to specialist services. As with any other clinical intervention, monitoring and screening intervals should be based upon solid evidence. Monitoring for microalbuminuria and screening for diabetic retinopathy less frequently would reduce the rates of false positives tests but the implications of less surveillance need further investigation.
Supervisor: Stevens, Richard; Matthews, David Sponsor: National Institute for Health Research School for Primary Care Research
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Medical Sciences ; Primary Care ; monitoring ; diabetes ; statistical models