Funding Source:
Health Data Research UK
Proposed start date:
September 2021
Closing date for applications:
31st May 2021
Eligibility:
UK/EU and International applicants (international applicants see funding section)
Department/School:
Health Sciences
Supervisors:
Dr Michael Sweeting [Email Address Removed]
Professor Paul Lambert [Email Address Removed]
Project Description:
Multistate models are an important statistical tool to understand disease trajectories and can provide clinically important measures of effect and effect differences(1). Multistate models can be used to describe patterns of multimorbidity using large-scale electronic health records and, through quantification of utilities associated with disease states (e.g. quality of life measures), the individual burden of multimorbidity can be estimated. Nevertheless, there are numerous computational and analytical challenges in this framework, with multiple comorbidities and disease pathways making multistate modelling demanding without model simplification.
This studentship will develop methods for multistate modelling in multimorbidity research, which will lead to
1) innovation in trajectory modelling and clustering of multimorbidity pathways.
2) methodological advances that underpin new data science developments in large-scale longitudinal linked electronic health records, as being curated in the Health Data Research Hubs.
The project will consider two important aspects in the developments of multistate models in multimorbidity research. Firstly, the development of models for complex disease trajectories, including approaches to relax the Markov assumption, including multiple time-scales in derivation of rates and incorporation of recurrent events. Secondly, the project will investigate efficient post-estimation simulation-based techniques to estimate clinically useful measures of effects and effect differences, such as disability- or quality-adjusted life-years and their contrasts. These measures are commonly calculated in health technology assessments using Discrete Event Simulation (DES) of multistate models (2). Variance reduction techniques such as cloning and antithetic sampling will be investigated to allow efficient estimation of incremental effects between two or more groups of interest.
These techniques will be applied to the investigation of cardiovascular and non-cardiovascular outcomes following a cancer diagnosis, utilising national data from the Virtual Cardio-Oncology Research Initiative (VICORI) (3). The student will develop user-friendly open-source software to encourage reproducible research and enable the methods to be utilized across health data research.
Funding details:
This 3-year PhD studentship provides:
· UK/EU tuition fee waiver
· Annual stipend rates as follows: 2021/22: £19,612, 2022/23 £19,906, 2023/24 £20,205
Entry requirements:
Applicants are required to hold/or expect to obtain a UK Bachelor Degree 2:1 or better or Masters degree in a subject that relates to the goals of the research group (e.g. Biostatistics, Health Data Science), or overseas equivalent qualification.
The University of Leicester English language requirements apply where applicable.
Application advice:
To apply please refer to the guidance at: https://le.ac.uk/study/research-degrees/funded-opportunities/hs-hdr-uk-sweeting-2021
Project / Funding Enquiries:
Dr Michael Sweeting [Email Address Removed]
Application enquiries to [Email Address Removed]
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