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Personal profile


Garima joined Manchester Centre for Health Economics (MCHE) as a Research Associate in 2018. She is currently working on a NIHR funded study (PrefCoRe) that aims to quantify patients' and public's preferences for the treatment of high-risk rectal cancer and build a patient decision aid to help rectal cancer patients make difficult decisions regarding their treatment. In the past, Garima has also worked on another NIHR funded study (STOpFrac) that involved conducting an early economic evaluation and headroom analysis for an automated computer system to detect vertebral fractures in CT scans taken in the NHS (ASPIRE). In addition to this, she has worked on a MRC funded study (PSORT) where she was involved in quantifying patient preferences regarding algorithm-based prescribing in psoriasis. 


Garima holds a MSc in Health Economics and Decision Modelling from The University of Sheffield and a BSc in Economics from Lancaster University. Prior to joining MCHE, she undertook her MSc dissertation project at a pharmaceutical company in the UK where the she conducted a cost-effectiveness analysis of achieving a stage shift in non-small cell lung cancer through a national awareness campaign and low-dose CT mobile screening units in Manchester.


BSc Economics (Lancaster University)

MSc Health Economics and Decision Modelling (University of Sheffield)

Research interests

  • Cost-effectiveness modelling
  • Statistical methods in health economic analysis
  • Preference elicitation

Memberships of committees and professional bodies

  • Health Economists' Study Group (HESG)

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  • SDG 3 - Good Health and Well-being


Dive into the research topics where Garima Dalal is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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