A member of the Quanticate Programming team writes about their opinions of the INTO statement in PROC SQL.
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A member of the Quanticate Programming team writes about their opinions of the INTO statement in PROC SQL.
Today, the Pharmaceutical industry, like many, has its feet in both camps when it comes to Big Data. Some parts of the industry, such as genomics and drug discovery, were early adopters and today couldn't imagine life without Big Data technologies and approaches. Others are pushing their current approaches to near their limits, and are beginning to consider "what's next?"
Nick Burch, CTO at Quanticate discussed Big Data in Clinical Trials at the 4th Annual Clinical Data Integration and Management conference this year in Princeton, NJ. His presentation is titled: 'The Myth of the Big Data Silver Bullet - Why Requirements Still Matter'
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!
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.
Often when we write SAS code in the pharmaceutical industry, there is a high level of repetition. This guide explains ways of writing repetitive SAS code using Excel that will reduce the overall time to write the code and make large scale amendments easier and quicker.
This slideshow focuses on the problems faced when working with large SAS datasets and ways to resolve these problems.
Numerous SAS® programmers experience problems when working with large SAS® datasets that have millions of rows, hundreds of columns and are close to the size of a gigabyte or even more.
