This research project provides an opportunity to combine big data analytics and advance translational research in ageing. The study will systematically investigate risk and benefits of using medicines in older adults. This is a collaborative project, which will involve working with Dr Hamish Jamieson from the Big Data and Ageing Research Group at Christchurch and Dr David Chyou from University of Otago based at Dunedin.
The overarching objective of this research project is to advance geriatric pharmacoepidemiology research, and to understand medication safety surveillance in older people who are invariably underrepresented in clinical trials.
In this study, we propose to analyse New Zealand’s pharmaceutical collections (pharms) and national minimum dataset (NMDS) and the world-leading interRAI data to identify drug-related adverse events leading to hospital admissions. The key advantage of using the pharms and national minimum datasets is that prescribing information from pharms including patient demographics can be linked to hospital admissions derived from NMDS and clinical data from interRAI.
The risk of adverse drug events in older adults is a pervasive problem and is complicated by the presence of polypharmacy, multimorbidity and geriatric syndromes. Current pharmacovigilance tools are inadequate to uncover important medication combinations contributing to adverse drug events. As a pharmacoepidemiologist, it is important to understand patterns of medication exposure combinations contributing to adverse drug events. Association Rules regarded as one such machine-learning algorithm that can be used to characterise the complexity of medication utilisation patterns including medication combinations, drug interactions, and to detect adverse drug reactions signals due to confounding observed in observational studies.
Modelling the risks and benefits of medicines is another important clinical decision criterion. The research project will examine the utility of application of a novel regression-based risk-benefit approach and examine further development in methodologies for benefit/risk analysis of medicines in older adults.
This simple algorithm of risk-benefit evaluation has the potential for the development of a decision-making software to assist clinicians to optimize medication safety and improve post-marketing surveillance.
Applicants should hold, or expect to receive, a First Class or high Upper Second Class UK Honours degree (or the equivalent qualification gained outside the UK) in a relevant subject. A master’s level qualification would also be advantageous.
During this project, there will be the opportunity to work with world-class researchers and big data analytics. Experience in high performance computing and using Python functions in R will be highly regarded.
Informal enquiries should be directed to Dr Prasad Nishtala, [email protected]
Formal applications should be made via the University of Bath’s online application form – link https://samis.bath.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUPA-FP01&code2=0013
Please ensure that you quote the supervisor’s name and project title in the ‘Your research interests’ section. Should you wish to apply for more than one advertised project, you should submit a separate personal statement for each one.
More information about applying for a PhD at Bath may be found here: http://www.bath.ac.uk/guides/how-to-apply-for-doctoral-study/
Anticipated start date: 30 September 2019.
1. Nishtala PS, Chyou T, Held F, Le Couteur DG, Gnjidic D. Association Rules Method and Big Data: Evaluating frequent medication combinations associated with fractures in older adults. Pharmacoepidemiology and Drug Safety, 2018; 27:1123-1130 [IF: 2.897]
2. Nishtala PS, Chyou T. Sequential pattern mining to predict prescribed medications following hospitalisation for heart failure. Pharmacoepidemiology and Drug Safety 2018, 27: 456. [IF: 2.897]