University of Cambridge > Talks.cam > Cambridge Mathematics Placements Seminars > Investigation into appropriate statistical models for the analysis and visualisation of data captured in clinical trials using wearable sensors

Investigation into appropriate statistical models for the analysis and visualisation of data captured in clinical trials using wearable sensors

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If you have a question about this talk, please contact Dr Vivien Gruar.

The current rapid evolution of wearable sensors and devices for the collection of health-related data is laying the foundation for the next revolution in clinical trial operations. Wearable health monitors offer capabilities to collect semi-continuous, accurate health data in near-real time. This emerging digital research platform has the potential not only to increase data accuracy and timeliness but most importantly enables the collection of ‘real-world’ data, providing insights into the effect of therapies on patients’ daily lives, ultimately allowing pharmaceutical companies to explain the value of their medications beyond traditional efficacy measurements.

At GSK we are investigating the use of wearables in our clinical studies, with specific focus on actigraphy (remote monitoring of physical activity through inertial sensors). A wide range of diseases – such as Rheumatoid Arthritis (RA) and Chronic Obstructive Pulmonary Disease (COPD) – have a negative effect on physical activity, affecting the amount, type and way that patients perform certain activities and manoeuvres. Using wearable physical activity monitors in clinical trials enables us to monitor patients’ physical activity and rest cycles regularly between clinical visits, however extracting meaningful clinical information (and interpreting this information) is a major challenge: the high-frequency time-series nature of the data together with the vast volume provided by wearables (and inertial sensors in particular) make this type of data completely different from any other clinical data generated in clinical studies and for that reason the most appropriate statistical mathematical methodologies and techniques to maximise the information extracted from the data are still to be determined. Additionally, early investigational studies have shown that the variability of the data due to patients’ different behaviours and lifestyles is significantly greater than other clinical data and therefore appropriate statistical models for data analysis and visualisation need to be further investigated.

Through this project, we would like to investigate suitable statistical models for the analysis and visualisation of clinical data from wearable devices, with particular focus on actigraphy data. The end goal is to assess the impact of a therapeutic intervention on patients.

This is a broad, open-ended project in which the student would be required to work closely with colleagues from two different departments: ‘Clinical Innovation & Digital Platforms’ which has as a remit of modernising GSK ’s clinical studies by enabling the introduction of novel digital technologies and ‘Statistical, Programming and Data Strategy’ which underpins GSK R&D’s ability to make high-quality quantitative decisions across medicine development lifecycle.

This talk is part of the Cambridge Mathematics Placements Seminars series.

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