The ICH E6 on Good Clinical Practice was updated on 9 November 2016, the first addendum for 20 years. Sections 5.0 on Risk and 5.18.3 on Extent and Nature of Monitoring in particular created an increased need for Risk-Based Monitoring (RBM) and Centralized Monitoring (CM). The details of how this may be covered and the increasing evolution of a risk in the industry have been noted. The required changes involved create a number of different team roles. These include opportunities for biostatisticians and statistical programmers - players who may not have been so directly involved in onsite monitoring, a more traditionally clinical domain.
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.
How can the efficiency of a Clinical Research Organization (CRO) be understood? As CROs provide various services to study sponsors, their overall efficiency at providing these services and internal processes to allow these to be delivered can be broken down into a number of elements with their own unique efficiency.
We've all heard the hype - Big Data will solve all your storage, processing and analytic problems effortlessly! Some moving beyond the buzzwords find things really do work well, but others rapidly run into issues. The difference usually isn't the technologies or the vendors per-se, but their appropriateness to the requirements, which aren't always clear up-front.
Big Data, and the related area of NoSQL, are actually a broad range of technologies, solutions and approaches, with varying levels of overlap. Sadly it's not just enough to pick "a" Big Data solution, it needs to be the right one for your requirements. In this talk, we'll first do a whistle-stop tour of the different broad areas and approaches of the Big Data space. Then, we'll look at how Quanticate selected and built our Big Data platform for clinical data, driven by the needs and requirements. We won't tell you what Big Data platform you yourself need, but instead try to help you with the questions you need to answer to derive your own requirements and approach, from which your successful Big Data in clinical trials solution can emerge!
European Pharmaceutical Contractor held an interview with Quanticate CEO; David Underwood, asking how he started in the pharmaceutical industry, the reasons behind Quanticate's recent success and future trends as well as regulations in the industry.
This video is presented by Kelci Miclaus from SAS JMP who was a speaker at Clinical Data Live 2013. Her presentation was is titled: 'Efficient Data Reviews and Quality in Clinical Trials'.
The statistical programming language R is often underrated within the Pharmaceutical Industry. Often the default is to pay for expensive software when R could be a viable option. R is freely available and runs on almost all operating systems including Unix, MacOS, and Microsoft Windows.
Tables, listings and figures are part of day to day clinical submissions. Hence it would be useful if statisticians/clients could easily analyse the study data through different time points. This allows for better decisions because you are able to view outputs while the study is on going, this visualisation during a study can allow for great efficiencies as decisions can be made earlier in the clinical trial.
In today’s environment, one of the keys to accelerating drug development decisions comes from ready access to historical data. The introduction of the Centralized Service Provision (CSP) enables data across study phases and programmes to be accessed as easily as within the same study, due to the commonality of structure.
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