Method agreement analysis – common pitfalls and a review of methodology

A common question in clinical research is whether a new method of measurement is equivalent to an established one. As a statistical consultant at PHASTAR, I am seeing an increase in the number of trials where a new artificial intelligence or machine learning 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 continuous 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, rather 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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How can we compare medicines from different clinical trials?

At a conference, the results of a clinical trial demonstrate that a new medicine is efficacious and safe for a particular disease or condition. Patients, understandably, want to know when they will have access to this new treatment. Regulatory approval will be sought and individual countries will need to consider reimbursement. But, before we even start contemplating delivery of the treatment or how much it may cost, we need to ask "efficacious compared to what?"

Usually, clinical trials compare a new treatment to a "standard of care", which may be a medicine currently used in clinical practice or a placebo. Ideally, the new medicine would have been compared to all existing treatments, including those in development. That sounds ridiculous, but, after a clinical trial has been completed, other treatment options may have become available (which may be different in different countries). It would be impractical to design and conduct new randomized clinical trials at this stage to compare every new treatment to all available treatments in such a constantly evolving landscape. Instead, we can employ indirect treatment comparisons to attempt to estimate differences between some of these medicines.

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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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