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4 year funded PhD studentship: Generalizability and transportability in clinical trials

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Title: 4 year funded PhD studentship

UCL Department of Statistical Science

University College London

Location: London

Funding for: UK/EU/International students

Funding amount: £19,062 plus Home/EU/International tuition fees, 4 years

Hours: Full Time

Closes: 24 June 2022

This is an EPSRC DTP-CASE studentship in partnership with Novo Nordisk. The awarded PhD student will be supported by a team of experts across UCL Statistical Science and Novo Nordisk. The student will also undertake a minimum of 3 months placement with Novo Nordisk during the PhD.

Project: Generalizability and transportability in clinical trials

One of the principal aims of a clinical trial program is to estimate the underlying causal effect of an intervention in the general population. The ‘gold-standard’ to quantify this is through a randomized controlled trial (RCT), whereby causal effects are assessed by comparing outcomes between the control and the intervention group. Often, however, the sample of RCT participants is not representative of the population in which the treatment is delivered. While the estimated causal effect will be internally unbiased for the RCT sample, it may not be externally valid for the target population. Observational studies, on the other hand, are typically more representative of the target population but can be subject to other internal biases due to confounding.

The aim of this PhD project is to assess and improve the generalizability and transportability of treatment effect estimates from clinical trials. To that end, the student will develop rigorous and principled statistical methodology to combine inferences from RCTs and observational studies. Registry-based RCTs offer particular promise for improved generalizability as participants are recruited directly from health and disease registries and are thus more representative of the target population. The student will assess the developed methodology through both extensive simulations and application to real clinical trial data.

Informal enquiries can be made to Dr Brieuc Lehmann (supervisor): [email protected]

How to Apply: The details of the studentship, including the eligibility requirements and application procedure are available from the link below.

UCL Statistical Science Studentships

Click Here to Apply For The Job



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