Emerging technologies and health data

Emerging technologies and health data

The possibilities of capitalizing 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 maximizing the potential to access data through electronic health records, mobile applications, and wearable devices.

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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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Missing values estimation in clinical time series

PHASTAR's Zara Ghodsi discusses a new method for the imputation of time series data based on singular spectrum analysis (SSA)

Missing data is a common problem in clinical trials despite all our best efforts to minimise it through design. It is likely to occur in most randomised controlled trials. When missing data is present, the ability to conduct intention-to-treat analyses, which require the complete inclusion of all data from all randomised patients, is compromised and can influence results. For this reason, much research is focussed on analytical techniques to estimate unbiased effects in the presence of missing data, including imputation-based methodologies. PHASTAR statistician Zara Ghodsi has recently had a paper published where she proposes a new method for the imputation of time series data based on singular spectrum analysis (SSA).[1]

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What about the smell? - Challenges of clinical trial design and maintaining blinding

What about the smell? - Challenges of clinical trial design and maintaining blinding

When designing a clinical trial, one of the biggest factors that needs to be thought about is blinding. In clinical trials, there is a risk of expectation influencing findings so if a patient knows which treatment group they have been allocated, there is a risk that this could bias results. In controlled trials, the term “blinding” refers to keeping study participants, those involved with their management, and those collecting and analysing clinical data unaware of their assigned treatment so that they cannot be influenced by that knowledge. If patients are not blinded, awareness of group assignment may affect their behavior in the trial, and their responses to subjective outcome measures.

Blinding may not always be easy or possible, and it goes much further than just keeping the name of the treatment hidden. Differences in appearance of the drug used in the study could give a clue to its appearance. Differences in taste, smell, mode of delivery, or even colour may also affect perceived efficacy, so these aspects should be identical in each treatment group. PHASTAR Senior Statistician, Stephen Corson, recently published a paper with the results from a study aiming to investigate whether adding levomenthol to an ibuprofen gel could reduce the time taken for an analgesic effect to occur in patients with soft-tissue injuries.[1] Menthol produces the sensation of cooling without reducing skin temperature and following topical application, menthol has an anaesthetic effect. Menthol can also enhance the skin penetration of topical analgesics, potentially increasing their effectiveness in relieving pain. But how do you blind menthol? That was the challenge that Stephen faced when designing the study procedures for this trial. Here he explains how these issues were addressed.

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