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SUMMARY:Investigation into appropriate statistical models for the analysis
  and visualisation of data captured in clinical trials using wearable sens
 ors - Dr Luis Garcia-Gancedo\, GSK
DTSTART:20180220T130000Z
DTEND:20180220T140000Z
UID:TALK100804@talks.cam.ac.uk
CONTACT:Dr Vivien Gruar
DESCRIPTION:The current rapid evolution of wearable sensors and devices fo
 r the collection of health-related data is laying the foundation for the n
 ext revolution in clinical trial operations. Wearable health monitors offe
 r capabilities to collect semi-continuous\, accurate health data in near-r
 eal time. This emerging digital research platform has the potential not on
 ly to increase data accuracy and timeliness but most importantly enables t
 he collection of ‘real-world’ data\, providing insights into the effec
 t of therapies on patients’ daily lives\, ultimately allowing pharmaceut
 ical companies to explain the value of their medications beyond traditiona
 l efficacy measurements.\n\nAt GSK we are investigating the use of wearabl
 es in our clinical studies\, with specific focus on actigraphy (remote mon
 itoring of physical activity through inertial sensors). A wide range of di
 seases - such as Rheumatoid Arthritis (RA) and Chronic Obstructive Pulmona
 ry Disease (COPD) - have a negative effect on physical activity\, affectin
 g the amount\, type and way that patients perform certain activities and m
 anoeuvres. Using wearable physical activity monitors in clinical trials en
 ables us to monitor patients' physical activity and rest cycles regularly 
 between clinical visits\, however extracting meaningful clinical informati
 on (and interpreting this information) is a major challenge: the high-freq
 uency time-series nature of the data together with the vast volume provide
 d by wearables (and inertial sensors in particular) make this type of data
  completely different from any other clinical data generated in clinical s
 tudies and for that reason the most appropriate statistical mathematical  
  methodologies and techniques to maximise the information extracted from t
 he data are still to be determined. Additionally\, early investigational s
 tudies have shown that the variability of the data due to patients’ diff
 erent behaviours and lifestyles is significantly greater than other clinic
 al data and therefore appropriate statistical models for data analysis and
  visualisation need to be further investigated. \n\nThrough this project\,
  we would like to investigate suitable statistical models for the analysis
  and visualisation of clinical data from wearable devices\, with particula
 r focus on actigraphy data. The end goal is to assess the impact of a ther
 apeutic intervention on patients.\n\nThis is a broad\, open-ended project 
 in which the student would be required to work closely with colleagues fro
 m 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\, Progra
 mming and Data Strategy’ which underpins GSK R&D’s ability to make hig
 h-quality quantitative decisions across medicine development lifecycle.\n\
 n
LOCATION:MR3 Centre for Mathematical Sciences
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