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The effects of risk factor measurement variability on clinical decision-making

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Clinical measurements are undertaken to diagnose patients and to determine eligibility for treatment. They are also undertaken to monitor response to treatment. Examples include measurement of blood pressure, glycosylated haemoglobin, lipid levels but there are many others. Each of these measurements shows within-individual biological or chance variation from one measurement to the next. This has profound implications for clinical decision-making. These implications are largely ignored by clinicians and by guideline authors. First it means that many individuals diagnosed on the basis of measurements are misclassified because of chance variation in measurements. Second, chance variation in clinical measurements mean that clinical judgements about whether treatments are effective are often incorrect, therefore decisions to change treatment are also often incorrect.

The supervisor has access to a large database of UK electronic primary care records. This includes measurements (blood pressure, glycosylated haemoglobin etc) on hundreds of thousands of patients and also the treatments they were prescribed. It is therefore possible to investigate the variability of measurements within individual patients and to identify the treatment decisions taken in those patients. The research will be undertaken in this database.

In large part this will be an analytic PhD thesis for someone with a good understanding of epidemiology who can undertake analysis of large datasets (i.e. statistical programming) and modelling the implications of variability. It will involve identifying measurements which might show variation and the treatment or clinical management decisions which might be affected. The analysis will then describe the degree of variation in these risk factors and undertake analysis to infer how measurement variability is affecting decision-making.

There may also be the opportunity to investigate how doctors interpret measurement variability, which could use more qualitative or mixed methods.

Before replying to this PhD proposal you must:
a) Read some of the references listed below. If you don’t find them interesting this is not the PhD for you.
b) Familiarise yourself with large databases of electronic primary care records (e.g. CPRD or THIN in the UK, SIDIAP in Spain, NIVEL in the Netherlands etc)

Funding Notes

This is self funding only


1. Bell KJL, Doust J, Glasziou P. Incremental Benefits and Harms of the 2017 American College of Cardiology/American Heart Association High Blood Pressure Guideline. JAMA Intern Med. 2018 Jun 1;178(6):755-757. doi: 10.1001/jamainternmed.2018.0310.
2. Bell KJ, Hayen A, Irwig L, Takahashi O, Ohde S, Glasziou P. When to remeasure cardiovascular risk in untreated people at low and intermediate risk: observational study. BMJ. 2013 Apr 3;346:f1895. doi: 10.1136/bmj.f1895.
3. Hayen A, Bell K, Glasziou P, Irwig L. A counterargument to encounter frequency and target achievement: measurement variability. Arch Intern Med. 2012 Feb 27;172(4):374; author reply 374-5. doi: 10.1001/archinternmed.2011.807. No abstract available.
4. Bell K, Hayen A, McGeechan K, Neal B, Irwig L. Effects of additional blood pressure and lipid measurements on the prediction of cardiovascular risk. Eur J Prev Cardiol. 2012 Dec;19(6):1474-85. doi: 10.1177/1741826711424494. Epub 2011 Sep 26.
5. Hayen A, Bell K, Glasziou P, Neal B, Irwig L. Monitoring adherence to medication by measuring change in blood pressure. Hypertension. 2010 Oct;56(4):612-6. doi: 10.1161/HYPERTENSIONAHA.110.153817. Epub 2010 Aug 9.
6. Bell KJ, Hayen A, Macaskill P, Craig JC, Neal BC, Fox KM, Remme WJ, Asselbergs FW, van Gilst WH, Macmahon S, Remuzzi G, Ruggenenti P, Teo KK, Irwig L. Monitoring initial response to Angiotensin-converting enzyme inhibitor-based regimens: an individual patient data meta-analysis from randomized, placebo-controlled trials. Hypertension. 2010 Sep;56(3):533-9. doi: 10.1161/HYPERTENSIONAHA.110.152421. Epub 2010 Jul 12.
7. Bell KJ, Hayen A, Macaskill P, Craig JC, Neal BC, Irwig L. Mixed models showed no need for initial response monitoring after starting antihypertensive therapy. J Clin Epidemiol. 2009 Jun;62(6):650-9. doi: 10.1016/j.jclinepi.2008.07.018. Epub 2008 Dec 23.
8. Bell KJ, Kirby A, Hayen A, Irwig L, Glasziou P. Monitoring adherence to drug treatment by using change in cholesterol concentration: secondary analysis of trial data.BMJ. 2011 Jan 21;342:d12. doi: 10.1136/bmj.d12.
9. Marshall T. When measurements are misleading: modelling the effects of blood pressure misclassification in the English population British Medical Journal 2004; 328:933.
10. Marshall T. Measuring blood pressure: the importance of understanding variation Brazilian Journal of Hypertension 2005; 12(2):75-82.

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