Methods for observational risk-benefit studies of medical devices: an analysis of big data and simulation studies
Lead Research Organisation:
University of Oxford
Department Name: Botnar Research Centre
Abstract
Medical devices are used in many areas of surgery. Recently published EU regulations will likely imply the need for numerous post-marketing device surveillance studies. There is however a scarcity of methodological literature on the performance of existing statistical models to minimise confounding in observational studies comparing the risk and benefit of different medical devices. Many methods exist and are widely applied in observational drug safety and comparative effectiveness research. However, there are challenges (e.g., surgeon characteristics, learning curves, revisions, device modifications) specific to medical device epidemiology.
We aim to assess the performance of different statistical methods for the observational study of the risk/s and benefit/s of medical devices, as used in actual practice conditions and in potentially all patients.
We will use routinely collected big health data, as well as simulated datasets. These will be analysed using different approaches, such as propensity score analyses, IP weighting, marginal structural modelling, interrupted time series, competing risks and novel ones resulting from methodological research. We will address practical questions in device epidemiology with clinical use case studies and assess methods performance in challenges arising from these use cases with simulation studies.
With this research we will create guidance on best methods to answer different challenges in observational post-marketing device surveillance research and give answer to actual clinical questions.
We aim to assess the performance of different statistical methods for the observational study of the risk/s and benefit/s of medical devices, as used in actual practice conditions and in potentially all patients.
We will use routinely collected big health data, as well as simulated datasets. These will be analysed using different approaches, such as propensity score analyses, IP weighting, marginal structural modelling, interrupted time series, competing risks and novel ones resulting from methodological research. We will address practical questions in device epidemiology with clinical use case studies and assess methods performance in challenges arising from these use cases with simulation studies.
With this research we will create guidance on best methods to answer different challenges in observational post-marketing device surveillance research and give answer to actual clinical questions.
Organisations
Publications
Alser O
(2020)
Serious complications and risk of re-operation after Dupuytren's disease surgery: a population-based cohort study of 121,488 patients in England
in Scientific reports
Alser O
(2020)
Serious complications and risk of re-operation after Dupuytren's disease surgery: a population-based cohort study of 121,488 patients in England.
in Scientific reports
Burn E
(2020)
Deep phenotyping of 34,128 patients hospitalised with COVID-19 and a comparison with 81,596 influenza patients in America, Europe and Asia: an international network study.
in medRxiv : the preprint server for health sciences
Burn E
(2020)
Deep phenotyping of 34,128 adult patients hospitalised with COVID-19 in an international network study.
in Nature communications
Burn E
(2021)
The natural history of symptomatic COVID-19 during the first wave in Catalonia.
in Nature communications
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
MR/N013468/1 | 30/09/2016 | 29/09/2025 | |||
2122671 | Studentship | MR/N013468/1 | 30/09/2018 | 31/12/2021 | Albert Prats-Uribe |