Dr Matthew Smith
PhD MSc BSc GradStat AMIMA
Matthew has experience in applying advanced statistical methods to answer epidemiological research questions regarding cancer patient health outcomes. His research involves investigating the patient and healthcare system interactions, evaluating interventions to reduce inequalities, and recommending key policy decisions.
In 2021, he received the Better Methods, Better Research award, jointly funded by the Medical Research Council and the National Institute of Health Research. During this 3-year programme he will advance methods in causal inference (e.g., targeted maximum likelihood estimation) and produce research for wider dissemination and reproducibility.
Matthew has a background in Mathematics and Statistics. He obtained his undergraduate degree (BSc) from Nottingham and a Masters (MSc) in Medical Statistics at the London School of Hygiene and Tropical Medicine (LSHTM). Following this, in 2017, he was awarded the Cancer Research UK Studentship in Cancer Survival, thereby funding his doctoral research on the socioeconomic inequalities in survival of patients with non-Hodgkin lymphoma; he completed his PhD in 2021.
Currently, he is a Postdoctoral Research Fellow of Biostatistics within the Inequalities in Cancer Outcomes Network at the LSHTM.
Matthew engages in the wider university community and has previously sat on the Student Representive Council for the position of Vice President for Doctoral Students.
Matthew is a co-module organiser for two MSc Medical Statistics modules:
1. Advanced Statistical Methods (Part 1: Causal Inference, Part 2: Dependent Discrete Data)
2. Robust Statistical Methods (rank-based, permutation, bootstrap, and sandwich methods)
He is a seminar assistant for the short course "Cancer Survival: Principles, Methods and Applications".
Matthew has co-supervised summer projects for MSc Medical Statistics students and is open to further collaborations.
Matthew's main interests are:
- Investigating the inequalities in cancer survival, with a focus on the interaction between the patient and the healthcare system
- Handling missing data in survival analysis studies, specifically for excess hazard models
- Advancing causal inference methods such as targeted maximum likelihood estimation
- Constructing emulated trials to explain socioeconomic inequalities in cancer patient outcomes