Many diseases and medical conditions, such as cancers, dementia and rheumatic disorders, are multi-factorial and may exhibit a range of complex biological phenotypes reflecting the contribution of a multitude of genetic and environmental components. Even with rare or ultra orphan diseases, such as Gaucher, which may be the result of mutations in a single gene, varying symptomatology, outcomes and treatment responses can be displayed across patients with the disease.The complexity of diseases leads to many challenges ranging from the understanding of disease mechanisms and disease susceptibility to risk prediction and the development and application of treatments.

Stratified medicine, where “homogeneous” groups of people likely to respond similarly to treatment or have similar underlying disease mechanism or outcome risk are sought based on molecular (biomarker) information, is a first step towards reducing biological variability and lies within the continuum of “patient therapy”, in which empirical or “all comer” medicine sits at one end and precision medicine at the other. The fact that genetic, molecular and imaging modalities have transformed biology from an observational science to a more data and computationally intensive quantitative science has meant that the need for those who are trained in handling data, accounting for uncertainty and making inference is ever more paramount. Many of the issues and challenges confronting stratified medicine are of a statistical nature.

We are undertaking a programme of research that encompasses risk stratification, prediction and validation, integrative and joint modelling of molecular and clinical data of various kinds and complexities (and their conversion into meaningful outputs that can inform health care decisions), mechanistic understanding and causality, treatment strategies and the design of innovative/purposeful clinical trials for biomarkers and dynamic treatment regimes.

MRC Biostatistics Unit
Institute of Public Health
University Forvie Site
Robinson Way
Cambridge CB2 0SR
United Kingdom

(Internal: Addenbrooke’s Box No. 113)

Since December 2001, I have been a research statistician at the Medical Research Council Biostatistics Unit in Cambridge. Prior to this I worked as a consulting statistician and project statistician within the (now defunct)  University of Cambridge Department of Public Health and Primary Care’s Centre for Applied Medical Statistics (CAMS) (1997-2000) and Epidemiology for Policy Group (2000-2001) respectively. I currently lead the Stratified Medicine Initiative at the Unit. Prior to this I worked within Professor Vern Farewell’s programme for “Statistical Modelling: theory and applications” which deals with methods of statistical inference for challenging data structures. However, I also collaborate with other senior scientists (present and former) in the Unit, in particular Professor Sheila Bird, Dr Daniela De Angelis, Professor Wally Gilks and Professor Carlo Berzuini.

Current research interest

• • Causal Inference

• • Chronic Disease Epidemiology (in particular related to Inflammatory Arthritis)

• • Dynamic Graphical Modelling of Stochastic Processes

• • Efficiency

• • Modelling of Longitudinal and Correlated Data

• • Multi-state Models and Survival Data

• • Stratified Medicine

Teaching

lecturer in the University of Cambridge Statistical Laboratory where I jointly taught (with Dr. Susan Pitts and Dr. Davide Pigoli) the Applied Statistics course on the Master of Mathematics/Master of Advanced Study (Part III of the Mathematical Tripos). I have also previously lectured on and been an examiner for the M.Phil in Epidemiology here at the Institute of Public Health, and in the past given a one-day course on “Handling Of Time-To-Event Data” for CAMS. I have also lectured and demonstrated on the European Union Sixth Framework Programme funded project Molecular Phenotype to Accelerate Genomic Epidemiology (MolPAGE) Training Course hosted in Pavia, Italy.

Below are my Applied Statistics Lecture Notes, Slides and R Practicals for Lent 2015

• • Lecture 1, Slides and Practical 1. (Survival Data Analysis). Accompanying Data-set: Leukaemia

• • Lecture 2, Slides and Practical 2. (E-M algorithm). Accompanying R function: EM.Normal

• • Lecture 3, Slides and Practical 3. (Modelling Correlated Non-Normal Data). Accompanying Data-sets: Epileptic Seizures and Respiratory Illness

• • Lecture 4, Slides and Practical 4. (Extending Linear and Generalized Linear Models).

I have also contributed encyclopaedic entries to the Encyclopaedic Companion to Medical Statistics.

Talks

Below is a talk I gave at the 2nd Clinical Trials Workshop at the MRC Biostatistics Unit on Subgroup Analysis

• • Introduction to Subgroup Analysis (18th March 2003)

Below is my internal MRC Biostatistics Unit presentation on interfacing R with C

• • How to link R with C (15th February 2005)

Below is a talk I gave to the Cambridge Statistical Discussion Group (CSDG) on Psoriatic Arthritis

• • Statistical Methods in Psoriatic Arthritis (3rd May 2007)

Below is my invited presentation at the 28th Annual Conference of the International Society for Clinical Biostatistics (ISCB28) in Alexandroupolis

• • A Mechanistic Approach to Bioinformatics – Integrative Genetics and Causal Modelling (1st August 2007)

Below is my presentation at the Armitage Workshop on Epidemiological Methods held at the Max Perutz Lecture Theatre, MRC LMB in Cambridge

• • Modelling Issues in the Clinical Epidemiology of Psoriatic Arthritis – Be Alert to Bias! (5th November 2009)

Below is my internal MRC Biostatistics Unit presentation on Time-Varying Confounding

• • Time-varying Treatment and Confounding (15th December 2010)

Links

Here is a list of links to people and projects that I have been associated with over the last few years.

• • My PhD supervisor Dr. Pat Altham’s web page

• • Dafna Gladman (research into rheumatic diseases, such as psoriatic arthritis)