Dr Matthew Smith


Research Fellow
in Biostatistics

Keppel Street
United Kingdom

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.


Faculty of Epidemiology and Population Health
Department of Non-communicable Disease Epidemiology


Centre for Global Chronic Conditions
Centre for Statistical Methodology


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

Research Area
Health inequalities
Health outcomes
Health services research
Statistical methods
Health services
Disease and Health Conditions
Non-communicable diseases
United Kingdom
Euro area
European Union

Selected Publications

The Delta-Method and Influence Function in Medical Statistics: a
Reproducible Tutorial
Zepeda-Tello R; Schomaker M; Maringe C; Smith MJ; Belot A; Rachet B; Schnitzer ME; Luque-Fernandez MA
Excess Mortality by Multimorbidity, Socioeconomic, and Healthcare Factors, amongst Patients Diagnosed with Diffuse Large B-Cell or Follicular Lymphoma in England.
Smith MJ; Belot A; Quartagno M; Luque Fernandez MA; Bonaventure A; Gachau S; Benitez Majano S; Rachet B; Njagi EN
Investigating the inequalities in route to diagnosis amongst patients with diffuse large B-cell or follicular lymphoma in England.
Smith MJ; Fernandez MAL; Belot A; Quartagno M; Bonaventure A; Majano SB; Rachet B; Njagi EN
British journal of cancer
Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorial.
Smith MJ; Mansournia MA; Maringe C; Zivich PN; Cole SR; Leyrat C; Belot A; Rachet B; Luque-Fernandez MA
Statistics in medicine
See more Publications