An international study to investigate and optimise the safety of discontinuing valproate in young men and women with epilepsy

Description

An exciting opportunity has arisen for a PhD student interested in Health Data Science to help us answer a critically important clinical question—probably the most significant one in epilepsy practice this decade.

Sodium valproate is a highly effective medication for controlling seizures in epilepsy. However, its use is restricted in women under 55 because it can harm their unborn child during pregnancy. Recent evidence indicates that valproate can also harm children born to men who take it, so these prescribing restrictions will soon apply to men under 55 as well. This means that many young men on valproate may soon be asked to stop taking it by their doctor, as is already happening for women. As valproate is such an effective antiseizure medication, stopping it could potentially harm these young men and women – but there is currently no evidence available to help counsel them on whether or not that truly is the case, nor to quantify what the level of risk is. Similarly, it is unclear what the safest alternative antiseizure medication is to replace valproate, meaning these young men and women are currently unable to have an informed discussion with their doctor about their options.

The successful applicant will join a team undertaking an international health data study to evaluate mortality and morbidity risks associated with valproate discontinuation. The study will emulate a hypothetical randomised-controlled trial (called a “target trial”) using retrospective observational data. The data will be drawn from ~250 million mainly US patients in the TriNetX database (which is a global network of healthcare organisations providing anonymised health data for research) and ~60 million UK patients in Clinical Practice Research Datalink (CPRD). These will be scanned for individuals aged 16–54 years with epilepsy and on valproate who either continued, switched to lamotrigine or levetiracetam, or discontinued valproate between 2014–2024, creating four groups. Randomisation to these groups will be emulated by baseline confounder adjustment using g-methods (e.g., inverse probability weighting). Mortality and morbidity outcomes will be assessed and compared between groups over 1–10 years, employing a time-to-first-event (Cox-proportional hazards model) and recurrent events analyses (e.g., the Prentice-Williams-Peterson-Total-Time Model). A causal (counterfactual) prediction model will be developed from these data to aid in predicting the safest alternative antiseizure medications.

Together, these findings will optimise informed decision-making about valproate withdrawal and alternative treatment selection, providing immediate and vital information for patients, clinicians and regulators.

A video summarising the study is available here.

The candidate need not necessarily have any prior experience in epilepsy/neuroscience. These skills will be gained during the course of the project. The project will provide a fantastic training framework for the successful applicant to develop their skills in health data science, particularly those in statistics (including causal inference methods), data engineering (including Structured Query Language (SQL)-based big data curation), and public engagement (including working with local communities directly affected by epilepsy). They will also gain a deeper understanding of neuroscience and drug pharmacology. There will be ample opportunity for high-impact journal publications for the successful applicant through this project, and it will be an invaluable platform for them to become a future leader in health data science if they wish to.

The PhD project and thesis will be embedded within the wider international health data project, allowing the successful candidate to be a vital member of the team delivering the project. The student will work with an experienced team at University of Liverpool (where the project is based) and University of Manchester (UoM, key collaborators on the project), including:

-       Dr Gashirai Mbizvo (primary supervisor): an NIHR Academic Clinical Lecturer in Neurology with a specialist interest in epilepsy, who is sponsoring this PhD through securing a highly competitive Emerging Leader Fellowship circa. £300k from the Epilepsy Research Institute, the largest charity in the UK dedicated to funding and supporting research into epilepsy.  

-       Dr Richard Jackson: a Senior Lecturer in Health Data Science who teaches on our Health Data Science MSc and is expert in causal inference – a statistician by background;

-       Professor Tony Marson: a consultant neurologist with a specialist interest in epilepsy, leader of the largest pragmatic randomised controlled trials in epilepsy SANAD 1 and 2, both published The Lancet;

-       Professor Iain Buchan: a public health physician and data scientist working to harness data and technologies for patients and population – Director of the Civic Health Innovations Lab (CHIL);

-       Professor Gregory Lip: a consultant cardiologist with health data expertise, author of the highly impactful CHA₂DS₂-VASc score, the most widely used predictive tool for stroke following atrial fibrillation globally;

-       Dr Glen Martin: Senior Lecturer in Health Data Sciences at UoM, world expert in clinical prediction models, using statistical methods in observational data, and evidence synthesis methodologies – a statistician by background;

-       Dr Matthew Sperrin: Senior Lecturer in Health Data Sciences at UoM, world-leading authority on the role of causality in prediction – a statistician by background;

-       Dr Laura Bonnett: Statistician primarily interested clinical prediction models for epilepsy, whose seizure prediction work has influenced DVLA driving policy;

-       Dr Pieta Scofield: data engineer, SQL expert.

Applicant Suitability

This PhD would most suit a candidate with any of the following skills or experiences at BSc/MSc level:

-       Health Data Science

-       Statistics

-       Mathematics

-       Health Informatics

-       R or Python 

-       SQL

-       Causal inference

-       Prediction modelling

Application Process

Application is via CV and cover letter (including details of any skills or experience in one or more of the items in Applicant Suitability) to the Project Supervisor email (Gashirai.Mbizvo@liverpool.ac.uk). Informal enquires can also be made to the same email address prior to making a decision to apply. Include “IRIS 182160 – valproate PhD project” in the subject line of application submission or informal enquires. 

Application deadline is 09/08/2024 (may close sooner if enough suitable candidates apply prior to that).

Interviews will be held over the subsequent weeks virtually, with a view to commencing the PhD around September 2024 (exact start dates are flexible). This is a 3-year, full-time post.