Emerging technologies and health data

Emerging technologies and health data

By Dr Sheelagh Aird, Head of Clinical Data Operations

The possibilities of capitalising on emerging technology in healthcare are endless. The drive for improved visibility and oversight, faster trial set-up, sharing of real-time data and easier stakeholder collaboration has led to a high implementation of EDC, eTMF, RTSM and CTMS across the clinical trials arena. Although the pharmaceutical industry has been comparatively slow to adopt and embrace new technology and eClinical applications, the pace of change is accelerating. There is a real opportunity to transform clinical trials, making them more pragmatic, patient-centric and efficient by maximising the potential to access data through electronic health records, mobile applications, and wearable devices.

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Method agreement analysis – common pitfalls and a review of methodology

by Prof. Jennifer Rogers, Head of Statistical Research at PHASTAR

A common question in clinical research is whether new method of measurement is equivalent to an established one. As a statistical consultant in PHASTAR, I am seeing an increase in the number of trials where a new AI diagnostic tool is being compared to either a pre-existing tool, or to a clinician. Methodology for the analysis of binary data is well established, but methodology for numerical outcomes is less developed. Here we shall review current methodology and outline some of the common pitfalls. It should be noted that concordance analysis doesn’t guarantee the correctness of methods of measurement, it shows the degree to which different measuring techniques agree with each other. To properly evaluate a new method of measurement, quantities pertaining to the validity of measures, such as sensitivity, specificity, and positive and negative predictive values should also be considered.

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Technology, AI and real world evidence

Technology, AI and real world evidence

by Dr Jennifer Bradford, Head of Data Science at PHASTAR

Real world evidence (RWE) in medicine is the clinical evidence regarding the use and potential benefits or risks of a medical product derived from the analysis of real-world data (RWD). RWD are effectively data collected from outside of a clinical trial and that relate data to the patient health status and/or the delivery of health care. RWD is routinely collected through different digital health sources for example electronic health records (EHRs), product/disease registries, patient-generated data, medical claims/billing databases, mobile devices etc.

Increasing volumes of RWD are being produced following the development of specialist devices and sophisticated data collection techniques.  Together with technological advancements including computing power and storage, there is an opportunity for powerful artificial intelligence (AI) approaches to be applied to these data to process and provide valuable insights for patient benefit. In the context of drug development, the application of AI to RWD and subsequent generation of RWE has huge potential with examples including analysis of patient treatment pathways, risk of disease development for patients, tracking patient behaviour’s and adherence.

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From Practical Maths to Superstar Stats – Zoe’s Story

I started out my university experience by going to Dundee to study mathematics, this was mainly a theoretical degree with the occasional statistic module in the first two years. Once I was in my fourth year, I decided I wanted to focus on the more practical aspect of mathematics. After looking around I found this new master’s course at the University of Strathclyde which took my interest, Applied Statistics in the Health Sciences. I started this course in the September and I loved it - I was so glad that I had gone down the route of statistics.

About five months into my master’s I started the application process for PHASTAR, which was very smooth. After emailing over my application I got offered an interview which was to take place in PHASTAR’s Glasgow office. At first, going along to the interview was a wee bit intimidating - this was my first experience of an interview which was in relation to my degree. But once we got into the swing of things there was a relaxing and sociable atmosphere in the room, which was a good insight into the company from the get-go.

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A quantitative framework to inform extrapolation decisions in children

A quantitative framework to inform extrapolation decisions in children

When developing a new medicine for children, the potential to extrapolate from adult efficacy data is well recognised. Extrapolation can be used to streamline drug development, with the European Medicines Agency (EMA) defining extrapolation as:

“… extending information and conclusions available from studies in one or more subgroups of the patient population (source population) … to make inferences for another subgroup of the population (target population) … thus reducing the amount of, or general need for, additional evidence generation (types of studies, design modifications, number of patients required) needed to reach conclusions.” [1]

This is essentially aiming to reduce the number or size of studies in a population, whilst still being confident to make inferences. Examples of extrapolation can include extrapolating from historical data to predict drug effects in contemporary patients, extrapolating from European patients to predict benefits in patients in Asia and, our focus here, extrapolating from adults to support licensing decisions in children.

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