I am a Senior Lecturer and researcher specialised in Medical Statistics.
After a PhD at Birmingham University, I was a Postdoctoral Fellow at the University of Leicester and at Harvard University. I then became a Research Associate at Harvard University and later joined the Boston Children’s Hospital.
My research seeks to answer questions related to clinical/genetic studies that arise in biomedical, public health and genetic research. Primarily, my previous research has focused on meta-analysis methods and applications for addressing problems that may hinder identification of prognostic factors which form a building block to enhance individualised risk prediction and clinical decision-making, and the identification of surrogate endpoints (intermediate endpoints) to predict final endpoints to facilitate health technology assessment and decision making to potentially replace the `costly' final endpoints with validated surrogate endpoints.
In the research area of statistical genetics, I have conducted research on local ancestry identification, captured using unsupervised learning methods on rare variants, to identify associations in genome wide association studies. Other work was focused on `optimizing’ surgical scheduling, in a Monte Carlo simulation framework using queuing theory principles.
My earliest research experience focused on efficient understanding of an epidemic process by utilising stochastic models that describe the disease's transmission. Computationally intensive methods were utilised to draw samples from the posterior distribution of the model's parameters including: Markov Chain Monte Carlo (MCMC), Sequential Monte Carlo (SMC) and Approximate Bayesian Computation (ABC).
Other research experiences include a wide range of applied and methodological work conducted at the Boston Children's Hospital Heart Center through providing research study design and statistical expertise related to clinical protocol development, research grant applications, building interactive web apps, and data analysis using a wealth of unique data sets.