Model-driven and data-driven solutions for regulatory and HTA decision-making to address emerging challenges in drug development in cancer


   Department of Population Health Sciences

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  Dr S Bujkiewicz, Dr Sam Khan  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Additional Supervisors: Prof Richard Riley, University of Birmingham; Dr Daniel Jackson, AstraZeneca; Dr Janharpreet Singh, University of Leicester

When new cancer therapies are developed, they are evaluated in clinical trials assessing treatment’s impact on patients’ outcomes. Long-term survival is an outcome typically of interest to decision-makers, who recommend which new treatments should be available on NHS. However, modern cancer therapies are often targeted to small subsets of patients who harbour a particular biomarker. Therefore, data from clinical trials evaluating the effectiveness of therapies in a cancer subtype may be limited. Other sources of data; based on alternative outcomes, study types or other cancers, may need to be synthesised efficiently for reliable policy decisions. You will apply a range of modern tools from biostatistics (including Bayesian statistics, meta-analysis and survival analysis), epidemiology and data science and develop novel approaches for evaluation of cancer therapies.

This project is part of an exciting collaboration with University of Birmingham and AstraZeneca. You will benefit from an experienced supervisory team with expertise in statistics and oncology and an industry partner. This PhD in Biostatistics will provide you with an opportunity to develop advanced analytical skills, gain insight into drug development and decision-making processes and influence important decisions in healthcare. A suitable candidate will have MSc in Statistics, Medical Statistics or a related discipline.

Enquiries

Project Enquiries to [Email Address Removed]

Programme enquiries to [Email Address Removed]

To apply please refer to

https://more.bham.ac.uk/mrc-aim/phd-opportunities/


Biological Sciences (4) Mathematics (25) Medicine (26)

Funding Notes

The competition funding provides students with:
4 years of stipend at UKRI rates
4 years of tuition fees at UK fee rates (Plus one award of a full overseas fee waiver to an international applicant)*
RTSG
Budget to help with the cost of purchasing a laptop
The University of Leicester will provide full overseas fee waivers for the duration of their study to all international students accepted at Leicester. The funder, UKRI, allows us to appoint up to 30% overseas students.

References

1. Ferrara R, Imbimbo M, Malouf R, Paget-Bailly S, Calais F, Marchal C, Westeel V. Single or combined immune checkpoint inhibitors compared to first‐line platinum‐based chemotherapy with or without bevacizumab for people with advanced non‐small cell lung cancer. Cochrane Database of Systematic Reviews. 2020(12).
2. Narayan, V., Kahlmeyer, A., Dahm, P., Skoetz, N., Risk, M.C., Bongiorno, C., Patel, N., Hwang, E.C., Jung, J.H., Gartlehner, G. and Kunath, F., 2018. Pembrolizumab monotherapy versus chemotherapy for treatment of advanced urothelial carcinoma with disease progression during or following platinum‐containing chemotherapy. A Cochrane Rapid Review. Cochrane Database of Systematic Reviews, (7).
3. Unverzagt S, Moldenhauer I, Nothacker M, Rossmeissl D, Hadjinicolaou AV, Peinemann F, Greco F, Seliger B. Immunotherapy for metastatic renal cell carcinoma. Cochrane Database of Systematic Reviews. 2017(5).
4. Goldkuhle M, Dimaki M, Gartlehner G, Monsef I, Dahm P, Glossmann JP, Engert A, von Tresckow B, Skoetz N. Nivolumab for adults with Hodgkin’s lymphoma (a rapid review using the software RobotReviewer). Cochrane Database of Systematic Reviews. 2018(7).
5. Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. American journal of epidemiology. 2016 Apr 15;183(8):758-64.
6. Papanikos T, Thompson JT, Abrams KR, Städler N, Ciani O, Taylor RS, Bujkiewicz S, A Bayesian hierarchical meta-analytic method for modelling surrogate relationships that vary across treatment classes. Statistics in Medicine 2020;39:1103–1124.
7. Poad H, Khan S, Wheaton L, Thomas AL, Sweeting M, Bujkiewicz S, The validity of surrogate endpoints in subgroups of metastatic colorectal cancer patients defined by treatment class and KRAS Status, Cancers 2022.
8. Schmitz S, Adams R, Walsh C. Incorporating data from various trial designs into a mixed treatment comparison model. Statistics in medicine. 2013 Jul 30;32(17):2935-49.
9. Jenkins D, Hussein H, Martina R, Dequen-O’Byrne P, Abrams KR, Bujkiewicz S, Methods for the inclusion of real world evidence in network meta-analysis, BMC Medical Research Methodology 21, 207 (2021)
10. S Bujkiewicz, J Singh, L Wheaton, D Jenkins, R Martina, K Hyrich, KR Abrams, Bridging disconnected networks of first and second lines of biologic therapies in rheumatoid arthritis with registry data: Bayesian evidence synthesis with target trial emulation, Journal of Clinical Epidemiology 2022; 150: 171-178
11. L Wheaton, A Papanikos, AL Thomas, S Bujkiewicz, Using Bayesian Evidence Synthesis Methods to Incorporate Real World Evidence in Surrogate Endpoint Evaluation (2022) Medical Decision Making.
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