I have recently completed my MSc in Epidemiology at the University of Bristol and graduated from the University of the West of England with a BSc in Mathematics. Through these courses, I have realised my goal of applying advanced statistics and mathematics in the field of epidemiology.
Network meta-analysis (NMA) is a method to pool published summary treatment effects from randomised controlled trials (RCTs) to obtain estimates of relative treatment effects between multiple treatments. NMA is routinely used to inform decisions as to which treatments are effective or cost-effective, but requires that the RCT evidence forms a connected network of comparisons. However, it is becoming more common that health care policy makers are confronted with disconnected networks of evidence.
Recently a multi-level network meta-regression (ML-NMR) method has been developed that relaxes this assumption, as long as individual patient data is available from one or more RCTs. ML-NMR fits a model for individual-level treatment effects in studies where there is individual patient data. This is achieved using copulae to approximate the joint distribution of effect modifiers and quasi-Monte Carlo integration to obtain the aggregate-level likelihood.
My project is on testing the validity of population adjustment methods for disconnected network analysis. The aim of this project is to extend the ML-NMR method for disconnected networks of evidence for a range of likelihoods, including likelihoods for survival outcomes, and to explore methods to assess the validity of the ML-NMR method in the context of disconnected networks of evidence.