'Heard' Immunity?

Amateur voices

The coronavirus crisis has been a humbling experience. To accompany the pandemic, we have had an epidemic of statistics. If I thought that after 45 years working as a medical statistician, with more than 30 years working on clinical trials, I had what it took to make sense of them all, I have been disabused: testing, diagnosis and infectious disease modelling are matters in which I have only ever had occasional involvement. Even in clinical trials, there are huge gaps in my experience. I have mainly dealt with therapeutic trials, rarely with prevention trials and never with vaccines. The reader is warned that mine is an amateur opinion.

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Medidata Migration

A biotechnology company, developing a novel paediatric gene therapy to treat a rare genetic immunodeficiency that keeps a child's immune system from functioning properly, made the bold decision to move an ongoing Phase III study away from a large full-service provider CRO who were delivering a less than optimal service.

As PHASTAR is a long-standing partner to this biotech company, providing for both data management and statistical services, the company made the decision to move the data management aspect of thise study from the full-service CRO to PHASTAR. The study was already actively recruiting, and subjects had already been treated and passed their year 1one visit with, their data being collected in Medidata RAVE.

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Thank you for your CV - we’d like to invite you for an interview!

Writing a CV is not easy, especially if you’re not used to selling yourself. With just one opportunity to demonstrate that you have the necessary skills and experience for the role you’re applying for, it’s vital to spend time creating a concise, tailored and well-presented CV that stands out from the crowd.

We’ve spoken to PHASTAR's talent acquisition team to get their advice on how to create a CV that maximises your chance of receiving the response: “Thank you for your CV – we’d like to invite you for an interview”!

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PHASTAR West Coast – Six Month Update

PHASTAR's San Diego office, our third in the US, just passed its six-month 'anniversary'. This office was set up to serve as a strategic operations hub for PHASTAR, further supporting our west coast sponsors and is a key component of our five-year plan. It has been an exciting, busy, and challenging year thus far – COVID-19 has modified our working world, and we continue to focus on thriving during these uncertain times. 

I joined the PHASTAR team to open the San Diego office, focusing on all aspects of PHASTAR’s strategies including business development, developing proposal and processes for delivery of clinical trial projects, staff development and recruitment. I also manage senior technical staff and am involved in oversight of multiple projects to ensure clinical trial reporting is delivered to agreed timelines with optimal quality.

Age is more than a number: Exposure–response modelling approaches for determining optimal dosing rules in children

Exposure–response modelling approaches for determining optimal dosing rules in children

PHASTAR statistician Ian Wadsworth has had a new research article published in Statistical Methods in Medical Research examining modelling approaches for quantifying how exposure-response parameters vary over different ages in paediatric populations. Here he summarises the methodologies considered: 

Within paediatric populations, different age groups of children treated with a new medicine may experience differences in dose–exposure and exposure–response (E–R) relationships due to age-related differences in growth, development and physiological differences [1]. One possible suggested age grouping by the International Council for Harmonisation (ICH) E11 document is preterm newborn infants; term newborn infants (0–27 days); infants and toddlers (28 days to 23 months); children (2–11 years); and adolescents (12 to 16–18 years, depending on region) [1]. However, such groupings are general suggestions, with specific treatments and therapeutic areas having the potential for differences. Our paper [2] developed approaches to estimate the E–R relationship in children and to identify age groupings which define practical and effective dosing rules.

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