Evaluation and development of methodologies for using real world evidence to investigate the results of randomised controlled trials, utilising COVID-19 vaccine trials as a worked example

Qualification: PhD

Department: Health Sciences

Application deadline: 22 August 2021

Start date: September 2021

Overview

Supervisors:

Project description:

In making treatment decisions clinicians must take into account evidence from relevant randomised controlled trials (RCTs). There is concern that external validity of RCT results are often poor, with results lacking generalisability to patient populations outside of an RCT setting, a perception that has led to underuse of treatments that are effective.(1) Unfortunately the results of RCTs will never be relevant to all patients and all settings, and hence the effectiveness of clinical interventions using real world evidence is becoming increasingly important for informing clinical practice. Notably, real-world data have shown the effects of therapies in populations not represented in randomised controlled trials. In addition, real world data have frequently demonstrated an “efficacy to effectiveness gap” in comparison to the results of randomized clinical trials, even when demographics of the observed populations are similar. Key reasons for differences observed include the clinical heterogeneity of populations, and length of follow up.(2) 

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the resulting disease, coronavirus disease 2019 (Covid-19), was first identified in Wuhan, China in December 2019. It rapidly developed into a global pandemic and as of March 18th 2021 there had been over 122 million cases reported worldwide, resulting in over 2.6 million deaths. For this proposed study we plan to use the example of vaccine trials for COVID-19. Multiple vaccine candidates have been rapidly developed during 2020, with some now being used in the general population, and others still under development. Published results from these vaccination trials will be used to investigate and expand on methodologies for comparing results from RCTs with real world evidence.(4,5) 

The aims of the PhD will be to:

i) Evaluate and expand on methods for matching an RCT sample with a sample from routine data, to see if trial results are replicated in a matched real-world cohort
ii) Determine reasons for the efficacy to effectiveness gap
iii) Evaluate and expand on methods for re-weighting trial results, where the patient group is often very selective, to assess the potential treatment results in a broader UK population.  
iv) Evaluate and expand on methods for selecting participants from routine datasets who represent those included in the RCTs, and compare their treatment results to those who were not included in the RCTs.

References:

1. Rothwell, P.M. External validity of randomised controlled trials: “To whom do the results of this trial apply?”, The Lancet (2005) Volume 365, Issue 9453, pp 82-93. DOI: 10.1016/S0140-6736(04)17670-8
2. C. Saunders, C. D. Byrne, B. Guthrie, R. S. Lindsay, J. A. McKnight, S. Philip, N. Sattar, J. J. Walker, S. H. Wild. External validity of randomized controlled trials of glycaemic control and vascular disease: how representative are participants? Diabet. Med. 30, 300–308 (2013). https://www.ncbi.nlm.nih.gov/pubmed/23075287
3. Worldometers. Coronavirus Update (Live) 2020 Available from: https://www.worldometers.info/coronavirus/.
4. Hartman E, Grieve R, Ramsahai R, Sekhon JS. From sample average treatment effect to population average treatment effect on the treated: combining experimental with observational studies to estimate population treatment effects. J. R. Statist. Soc. A (2015) 178, Part 3, pp. 757–778 (https://www.jstor.org/stable/43965761) 
5. Phillippo, D.M., Ades, A.E., Dias, S., Palmer, S., Abrams, K.R., Welton, N.J. NICE DSU Technical Support Document 18: Methods for population-adjusted indirect comparisons in submission to NICE. (http://nicedsu.org.uk/wp-content/uploads/2017/05/Population-adjustment-TSD-FINAL.pdf)

 

 

 

 
 

Funding

Funding

This 3-year PhD Studentship provides:

  • UK/EU tuition fee waiver
  • Annual stipend rates as follows:
    • 2021/22: £19,612
    • 2022/23: £19,906
    • 2023/24: £20,205

Entry requirements

Entry requirements

Applicants are required to hold/or expect to obtain a data science related degree e.g. computer science, informatics, maths, statistics of 2:1 or better (or overseas equivalent), and preferably also an MSc qualification in an appropriate subject e.g. medical statistics, bioinformatics, biostatistics, health data science. 

The University of Leicester English requirements apply where applicable

Informal enquiries

Informal enquiries

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How to apply

How to apply

To submit your application, pleaseuse the 'Apply' button below and select September 2021 from the dropdown menu.

With your application, please include:

CV
Personal statement explaining your interest in the project, your experience and why we should consider you
Degree Certificates and Transcripts of study already completed and if possible transcript to date of study currently being undertaken
Evidence of English language proficiency if applicable
In the reference section please enter the contact details of your two academic referees in the boxes provided or upload letters of reference if already available.

In the funding section please specify that you wish to be considered foran Health Data Research UK Studentship.

In the research proposal section please provide the name of the supervisors and project title (a research proposal is not required).

 

Eligibility

Eligibility

UK/EU candidates only