FREE PhD study and funding virtual fair REGISTER NOW FREE PhD study and funding virtual fair REGISTER NOW

Reducing Confounding by Unmeasured Variables in Observational Studies.

   Faculty of Biology, Medicine and Health

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Measuring the effect of an exposure on an outcome from observational data can be challenging, due to the effect of confounding. Regression modelling and propensity scores can be used to eliminate the confounding effect of measured variables, but they will not help to reduce confounding by unmeasured variables. Instrumental variables methods can remove the confounding due to unmeasured variables, but identifying a suitable instrument can be difficult, and if the association between the instrument and the exposure is not strong, the estimate of the effect of exposure will be imprecise.

More recently, alternative approaches to eliminating confounding by unmeasured variables have been proposed, such as propensity score calibration, the missing cause approach, and amplified confounding-based confounding estimation, as well as the ‘rule out’ approach to sensitivity analysis. In addition, it has been suggested that subjects treated against expectation have the highest likelihood of unmeasured confounding, and removing such subjects from the analysis can reduce bias in the estimated effect of exposure.

This PhD project will involve evaluating the above methods, and new methods developed by the student, to simulated data containing unmeasured confounding of different types and magnitudes. The student will identify the strengths and weaknesses of the various methods, and the most appropriate method to use in various situations. They will also apply the methods to previously analysed real data, to estimate the potential for unmeasured confounding in published estimates.

This project would require a motivated, intelligent candidate with a good degree (2.1 or 1st) in mathematics, statistics or similar subject. Some exposure to medical applications of statistics or a relevant MSc would be very useful. There is likely to be a considerable computer programming element to the project, so previous experience in this area would also be of benefit. 

Entry requirements

Candidates are expected to hold (or be about to obtain) a first-class honours degree (or equivalent) of distinction at masters level in physics or medical physics or a closely related area/subject.

How To Apply

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website ( Informal enquiries may be made directly to the primary supervisor. On the online application form select PhD Cancer Sciences

For international students, we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit

Funding Notes

Applications are invited from self-funded students. This project has a Band 1 fee. Details of our different fee bands can be found on our website View Website
Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website View Website


Lunt M, Glynn RJ, Rothman KJ, Avorn J &Stürmer T, Propensity score calibration in the absence of surrogacy, American Journal of Epidemiology, 175:1294–1302, 2012.

Email Now

Search Suggestions
Search suggestions

Based on your current searches we recommend the following search filters.

PhD saved successfully
View saved PhDs