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Tara Seedher

BS in Mathematics and Statistics ------------------ MSc in Statistical Sciences


DPhil Student

Improving primary care for Lymphoma cancer patients by exploring challenges in the diagnostic pathway

DPhil student in Cancer Science

DPhil thesis

The research aim of my DPhil is to facilitate rapid referral and diagnosis of lymphoma patients to improve clinical outcomes by:

  • Quantifying features and test results associated with time to diagnosis of lymphoma.
  • To understand the role of tests in primary care.
  • To investigate the use of new approaches, tests and technologies in the diagnostic pathway.

Supervisors

Clare Bankhead, Jason Oke, Graham Collins and Brian Nicholson

Background

I am a Statistician, who completed their undergraduate degree at Queen Mary’s University London (QMUL) in Mathematics and Statistics in 2020. I worked as a statistician intern at The Wolfson Institute of Preventive medicine with Dr Stephen Duffy in the Summer 2020. I worked on improving early diagnosis of Colorectal Cancer (CRC) by optimising the criteria for secondary care referral using the non-invasive diagnostic blood test FIT (Faecal Immunochemical Blood Test). The internship sparked an interest in medical research as I enjoyed being able to apply my statistical skills to positively influence the delivery of healthcare for cancer patients – for example, managing the frequency of false positive and negative FIT test results by balancing sensitivity and specificity rates. I analysed the 2014 CRC FIT pilot dataset, estimating mean sojourn time, interval cancers and over diagnosis across various FIT thresholds. The paper which I co-authored involved making recommendations in response to the colonoscopy capacity crisis following COVID-19.

 

From 2021-2022, I studied a MSc in Statistical Sciences at Oxford University, from which I gained extensive experience in data analysis and model fitting. As I have an interest in exploring the causation of heterogeneous responses to diseases, I decided to carry out my dissertation to examine the statistical association between mutation signatures and severity of COVID-19. I analysed SARS-CoV-2 sequences of varying severity using methods such as regression, ANOVA and non-parametric tests. I explored the occurrence of mutations in Ribonucleic acid (RNA) acid viruses and the importance of monitoring these due to the potential creation of newly created variants, which can impact on disease severity and transmissibility.

 

I was prompted to apply for the Cancer Science DPhil programme to specialise in academic medical data research. I am currently working with the Nuffield Department of Primary Care Health Sciences on my DPhil, amongst primary healthcare researchers, as well as receiving seminars and training from the Oxford Cancer Science Centre.

Alongside research, teaching statistics is one of my long-term visions. I have a strong desire to make statistical concepts understandable and enjoyable, by contextualising its value to motivate study, such as in healthcare, where disease mortality can be reduced.