The new Medical Device Regulation (MDR) addresses patient safety by mandating supporting clinical evidence for the sale and clinical use of medical devices. However, it places pressure on medical device manufacturers to supply regulators with robust clinical evidence to obtain regulatory approval of new devices they develop. Regulatory compliance is therefore more stringent, requiring more effort on part of both regulators and manufacturers. This thesis project explored the possibility of developing a new method to be used as a regulatory decision-making tool for the approval of newly developed knee implants. A Bayesian probabilistic method was developed and proposed. This method utilises existing arthroplasty registry and arthroplasty registry annual report data to determine if a newly released implant variant is “good”, where a “good” implant is one which meets standard regulatory thresholds in term of revision risk. This method was implemented on two datasets which were selected based on conditions of granularity, measures of implant performance, and accessibility for the thesis project. Some advantages and limitations of this method as a regulatory tool were proposed, the limitations being based on a clinical appraisal framework. Based on this it was concluded that the method indeed showed potential as such a regulatory tool, but needs further improvement due to considerations of reliability, precision, and the probabilistic nature of the results it yields. Both surgeons and patients can also potentially stand to benefit from this method. Aside from the regulatory context, also, the method has limitations in the fact that it does not account for other factors that may be responsible for complications related to knee arthroplasty surgery. Overall, however, this thesis presents a novel approach towards estimating the performance of knee implants from existing clinical data.

E-bikes have gained global popularity due to their environmentally friendly and sustainable attributes, as well as their ability to provide fast speeds and power assistance. However, the increasing popularity of e-bikes has introduced conflicts and crashes with other road users, especially the interaction with conventional bikes, as e-bikes and bikes share the same cycling infrastructure. To study the perceived risk of interaction with e-bikes, this research delves into the various factors, in terms of traffic environment, bicycle type, and individual factors.
This research has used the video-based survey method to measure the perceived risk of cyclists and presented traffic environment and bicycle types in hypothetical traffic scenarios with pre-recorded videos. A questionnaire is designed to investigate participants` perceived risk of hypothetical bicycle interactions and their personal information on demographics, cycling experience, competence of riding skills, cycling behaviors, and expectancy on e-bikes. Moreover, the perceived risk is measured by two items separately: the rate of perceived risk and the likelihood of being involved in a crash. After recruitment and data collection, the effects of various factors have been estimated by the random-effects ordered logit model.
Results implied that compared to interaction between conventional bikes, riding on an e-bike when encountering a conventional bike decreased the perceived risk. Moreover, riding at peak hours and having a conflict with left-turning cyclists was found to increase the perceived risk, while riding at a large intersection was found to decrease the perceived risk. In terms of individual factors, the experience of bicycle crashes and the preference to use an e-bike were found positively related to the perceived risk. While the competence of cycling skills and age were found negatively associated with perceived risk. Additionally, it was found that traffic environment factors were more prominent in predicting the rate of perceived risk, while individual factors had a stronger influence on predicting the likelihood of being involved in a crash.
These findings provide insights into the influence of e-bikes in shared cycling spaces, underscore the importance of traffic management and safety measures in crowded bicycle traffic, and emphasize the significance of intervention and educational initiatives for cyclists to decrease the perceived risk and enhance cycling safety.