I am a Statistician within the Inequalities in Cancer Outcomes Network at the LSHTM. I obtained my PhD from University of Delhi in 2018, which focused on developing statistical inferential procedures for data based on extreme ordered statistics from various lifetime distributions. Prior to this, I obtained a MSc and BSc in Statistics from Sri Venkateswara College, University of Delhi.
Currently, I am funded by the European Union’s Horizon 2020 research and innovation programme, to investigate and map the determinants of patients’ health related quality of life using Patient Reported Outcome Measures, following treatment with immunotherapy. I am also funded by the Pancreatic Cancer Research Fund, to develop a risk score for early detection of pancreatic cancer using machine learning methods applied to linked routine data.
Affiliations
Teaching
I am a co-module organiser and a tutor for the Statistics for Epidemiology and Population Health module taught on various intensive MSc courses at LSHTM.
I am also a tutor for Epidemiology of Non-Communicable Diseases module and distance learning modules for MSc Epidemiology students, namely (i) Statistics for Epidemiology (ii) Project Reports. I was a tutor on Robust Statistical Methods, a module specific to the MSc Medical Statistics, prior to the recent changes in the structure of this MSc programme.
I am a supervisor for MSc Medical Statistics and MSc Epidemiology summer projects.
Research
I have experience in applying advanced quantitative statistical methods and machine learning tools in the field of cancer epidemiology.
In a 3-year project funded by the European Union’s Horizon 2020 research and innovation programme, I am investigating the patterns and determinants of patients’ health related quality of life using Patient Reported Outcome Measures, following treatment with immunotherapy among a heterogenous cohort of patients from Spain, Portugal, France and the Netherlands. This will help in understanding the complex relationship between treatment regimens, patients’ characteristics, immune-related adverse events and quality of life, which will contribute to the advancement of long-term management of patients.
In a study funded by the Pancreatic Cancer Research Fund, I am using machine learning methods on linked routine data from England, to assess its utility and cost-effectiveness for targeted pancreatic cancer screening. This will help increase the number of pancreatic cancer patients diagnosed at an early stage, improving their options for treatment and survival.
Selected Publications
