Rheumatoid Arthritis (RA) is a long-term inflammatory disease that causes pain, swelling,
stiffness and loss of function in joints, with an approximately 0.5 to 1% (and increasing) prevalence in adults worldwide. Alongside many treatment options, there’s recently been an increased focus on producing biosimilars, with many new drugs expected to come to the market in the coming years [1].
The vast majority of pharmaceutical companies and Clinical research organizations (CROs) ask for an MSc in Statistics (or Medical Statistics) when hiring statisticians, claiming these degrees in particular provide adequate preparation and the necessary hands-on experience to work in the industry. However – how strict is this rule across different companies, and should this be a requirement? Can similar qualifications such as an MMath or PGDip (which can have a lot of crossover with traditional statistics MScs) be equivalent and provide the required statistical training for a successful career in industry? Or can a BSc be sufficient? I aimed to answer these questions by asking senior statistical leaders and recruiters for their views through 4 questions.
Nowadays, vast amounts of data are collected during any clinical trial and it is essential for pharmaceutical sponsors to understand these data in great detail to make accurate decisions.
At the present time, the regulatory and drug development communities are adapting to a rising trend in biosimilar development in a number of therapeutic areas.
The regulatory framework for biosimilars in the US is still evolving; the number of biosimilars approved by the FDA in 2015 was 1, this rose to 4 in 2016 and is set to increase in 2017. The FDA is developing and consulting on draft guidance documents that will shape future trials, and at this early stage there are a number of legal issues to be agreed around licencing conditions, such as the period of exclusivity and the applicability of the biosimilar to all approved indications of the reference product. To put this in context with the European landscape, biosimilars have been approved and used in the EU for over a decade without highlighting any major safety concerns. As of April 2017, there were 28 approved biosimilars in the EU on 11 different biologics. However, there are aspects of the emerging FDA guidance that will almost certainly be reflected in the evolution of trial designs in the future, for products aimed at the US market.
As a statistical programmer at a leading data focused Clinical Research Organization (CRO), we are requested to become involved with many programming activities on a daily basis, centred around dataset or display generation and quality control (QC). Having the opportunity to develop a process/system which can be used by others is rare. To build any system, a lot of in depth thought is required before any programming begins. We take the requirements and build a robust system to address each potential scenario that may arise, including some which should not.
Within this blog we will explain how statistical programmers can work through a simple request to build a robust system using logic, SAS, UNIX and experience. It will provide a flavour of how robust systems are built and other considerations.
A member of the Quanticate Programming team writes about their opinions of the INTO statement in PROC SQL.
Wearable devices are currently growing hugely in popularity, with predictions that the market will grow to $25 billion by 20191. Many of these devices, like the FitBit or Jawbone, are fairly cheap and affordable to the public. With the rising prevalence of chronic conditions like obesity due to our increasingly sedentary lifestyles, the use of wearable technology is on the up. Although initially marketed to consumers wanting to track their health and fitness, many wearable medical devices are now being designed and their potential use in clinical trials could completely transform and revolutionise the pharmaceutical industry. The obvious benefits to incorporating wearables in clinical trials are a higher compliance rate and reduced dropout rate, because wearing a device to monitor various vital signs and endpoints can reduce the need for hospital visits. For the same reason, clinical trials could have a much higher uptake and recruitment rate. The large amount of additional data could mean a lower variability, so fewer subjects could be needed to achieve statistical power. However, this concept is virtually brand new and has major questions that need to be answered before real progress in this area can begin.
In any clinical trial conduct, recording and storing data in a controlled, consistent, and reproducible manner for data retrieval and analysis is a necessity for regulatory compliance and clinical study success.
The ICH Guidelines for Good Clinical Practice (E6) has morphed into the international standard for clinical trial conduct since its inception twenty years ago. However, within that period, clinical research has changed dramatically and it is a credit to it’s authors that ICHGCP remains relevant today, with only one previous notable update seen in response to electronic data capture (EDC) technologies being widely utilized by the industry.
