Methods for observational risk-benefit studies of medical devices: an analysis of big data and simulation studies

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.