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.” 
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.
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).
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. 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.
Starting a career in industry can be a daunting experience at any time of life, but especially when it is your first full time job. Going from being a full-time student and enjoying the student life is very different to experiencing the 9-5 day job. From my time so far, I believe that the first few years in industry can shape the direction that a statistician takes. While at university I was mainly surrounded by peers of the same age and level of experience - now that I’m in industry, I have found I have been able to draw on the different levels of experience and areas of knowledge there is in PHASTAR. Here I will discuss the decisions I made while at university which lead me into statistics and subsequently working at PHASTAR, along with some of my experiences from my first year.